The State of VFX and Other Multi-Asset Workflows and AI
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The State of VFX and Other Multi-Asset Workflows and AI

How AI Is Changing The Post-Production, VFX, and Media Asset Management. A Full Analysis.

Brandon Fan
Brandon Fan

I. Introduction

A. Introduction to VFX and Multi-Asset Workflows

Brief overview of the VFX industry and its challenges

Visual effects(VFX) is a dynamic industry in constant change through technological advancement displayed through movies, commercials, games, and diverse visual representations across media outlets. Since the first use of special effects in the late 1800s, VFX is now incorporated in approximately 90% of digital percent of digital media. The Global VFX Market is growing in demand rapidly with the increasing use of digital video streaming outlets like Amazon Prime, Netflix, etc. This pushes online video content to account for more than 82% of global web traffic in 2023 and expects digital video viewers worldwide to reach 3.5 billion this year. VFX is just one part of the bigger picture. There are other areas, such as 3D art and design, FX (special effects), SFX (sound effects), videography, as well as architectural and game-related projects, among others, that also encompass video content. With the growth of digital media outlets, the demand for VFX and other forms of media is increasing exponentially. As a result, artists are busier than ever with more media being produced and worked on.

From a single artist creating 100 FX Shots to teams working on over 3000 VFX shots for a blockbuster, the industry faces an increased workload and the necessity to ingest and manage numerous different capture formats and digital files. This means for some artists there are decades worth of footage and objects or terabytes of valuable assets in a single library. At the time of this publication, there have been strikes and calls for unionization from VFX artists; it’s now more important than ever that VFX teams and creative agencies prioritize artist and company morale through both pay and efficiency optimizations. After all, increasing artist productivity results in better clients and more revenue.

The state of Visual Effects (VFX) and Animation is currently marked by a dynamic evolution driven by technological advancements. These trends are reshaping the industry’s landscape with a focus on realism, physical prototyping, and automation. After all the greatest VFX is the VFX that can’t be seen. Virtual Production has emerged as a transformative trend, employing real-time technology to craft virtual environments and characters, streamlining post-production processes. Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing tasks such as rotoscoping and generating lifelike simulations of natural phenomena. Real-time rendering, facilitated by robust GPUs and game engines, accelerates the creative process and enhances collaboration. Cloud computing enables cost-effective scalability and remote collaboration, while Augmented Reality (AR) and Virtual Reality (VR) offer immersive audience experiences.

The integration of artificial intelligence (AI) into the animation and visual effects (VFX) industry is revolutionizing artistic processes and reshaping creative landscapes. Yet despite the craze of “generative AI,” AI’s automation of tasks such as generating textures and animations, crafting facial expressions and styles, or even rotoscoping has yet to be developed, simply due to poor performance and limited technical and hardware capabilities. However, there are a few notable uses of such AI. Character animation benefits immensely, with AI analyzing existing animations to swiftly create new ones, accelerating production and enabling experimentation. Collaborative roles such as AI animators and AI VFX specialists emerge, bridging creative and technical domains. The democratization of AI tools empowers independent artists and smaller studios to create quality content, yet challenges arise, prompting professionals to adapt and acquire skills that complement AI. Ultimately, AI’s integration empowers innovation while urging the industry to navigate evolving dynamics and nurture a balanced synergy between human creativity and technological advancement.

In the media, there has been significant attention given to the incorporation of AI into the VFX Pipeline, particularly focusing on Generative AI. Numerous tools have emerged within the creative workflow, aiming to revolutionize processes such as compositing, rotoscoping, tracking, and the overall 3D and post-production pipelines. The promise of AI in these workflows was initially hyped as a means to save time and reduce costs. For instance, software applications like Adobe After Effects have introduced AI tools for depth perception, Houdini’s integration with OpenAI’s ChatGPT allows for quick prompts in rendering and 3D model design, and Unreal Engine showcases the potential of AI in shaping the future of gaming with AI-controlled non-playable characters (NPCs). Generative AI has shown some promise in automating the creation of intricate 3D models, particularly for repetitive elements like foliage or architectural components. This is particularly valuable in scenes requiring a large number of object placements. Moreover, the influence of AI extends to other multimedia assets, such as audio, where generative AI attempts to compose sound and melodies that mimic human composition.

However, it is essential to note that while Generative AI has made notable strides in assisting creative processes, it has not lived up to the initial hype and is still far from replacing human artists. The technology faces limitations in understanding nuanced artistic decisions, emotions, and contextual subtleties that artists bring to their work. While AI can automate certain repetitive tasks and generate content, it lacks the depth of creativity, intuition, personal expression, and general reasoning that human artists possess. Thus, while Generative AI tools have their merits, they are not a substitute for the irreplaceable role that human artists play in the creative process.

Contrary to media belief, using AI to replace artists and employees is not something the decision-makers and VFX artists are looking to do anyway. Utilizing AI effectively will create cost and time-saving decisions. Targeting AI on the core frustrations of organization, data management, and file management is another layer of workflows that should be targeted to eliminate search time, cut time on repetitive tasks, and utilize file data and metadata to create better experiences for artists, studios, and others. Multi-media file management can be taken to the next step, and from there, many things can be fixed through this. AI-powered automated metadata generation can reduce manual labor, leading to substantial time and labor cost savings. Quick access to organized assets can improve overall productivity, shortening project timelines and potentially increasing project turnover. AI-driven visual search capabilities for assets can enable efficient browsing and previewing of diverse file types. AI can streamline interactions between different software in the VFX and creative industries, reducing manual data transfers and format conversions. By targeting these core frustrations, AI can help create better workflows for VFX and other multi-asset work by saving thousands of hours and tens of thousands of dollars a year.

B. VFX and Multi-Asset Workflow Pipelines

Explanation of typical multi-asset workflows

The VFX workflow is complex and is often garnered and classified under the term of “the pipeline”. This pipeline encompasses a wide array of components, and are highly dependent on the VFX company and the team that designed it from the very beginning. Fundamentally, the pipeline exists because companies use a wide array of technologies that each have to talk with each other. The average artist highly specializes in one, but is incredibly familiar with many. Common tools here would include:

  • Adobe Substance Painter
  • Autodesk Maya
  • Autodesk Flame
  • Nuke for Compositing
  • Houdini
  • 3DS Max
  • Unreal Engine
  • Blender
  • Shotgrid or F Track for Project Management

And the list can go on for highly specific operations like Mocha for Rotoscoping. What is duly noted about all of this software is their own unique file types and innate metadata that need to be handled, propagated, and maintained across multiple applications. For example, 3D assets from Blender must be properly versioned and used in the final composite in Nuke. Textures from Substance Painter must be properly handled into Houdini. Image Sequences, rendered at multiple resolutions and a variety of passes must be properly stored in the correct color space so that another part of the pipeline can read it.

And, arguably a part of the pipeline that is least managed but just as important, all assets, materials, image sequences, final renders, references, licensed assets, USD scenes, and Maya files all have to be properly archived and stored for reuse, reference, and marketing.

This last piece of asset library archival and asset library management is one that is often missed by many VFX studios that we have discussed, and often rely on highly manual workflows of tagging, custom-constructed asset browsers with limited previews, and large amounts of infrastructure just to manage a simple database and search engine (where search can just as much be useless due to changes in the filename per pipeline conventions).

This leads to petabytes of data that is often simply stored on hard drives in “cold storage” on-prem, without any knowledge of what is in the hard drives themselves.

The first thing to note about the pipeline is how many things must come together to make it work. Artists are very familiar with working with multiple bash scripts, stringing together a wide variety of operations. However, supervisors and production teams are typically unfamiliar with these workflows, leading to confusion about bottlenecks and what can or cannot be done. Proper visibility into a pipeline is imperative for smooth sailing; knowing where each and every item is in the pipeline of things and using a project management tool like Shotgrid or ftrack can save loads of time.

Part of this process around visibility into the pipeline is active previews. Previews are an imperative part of the “visual creative” process after all. Previews, in the technical sense, a quick images or 3D turntable renders of a scene, asset, or final composite. Oftentimes, studios skip out on this process as it’s tedious or simply infeasible with the tools that they use (for example Shotgrid natively does not support previews for almost any of the technologies that VFX studios use). This becomes a serious bottleneck when it comes to review, oftentimes catching issues with characters, assets, and scenes before it’s too late and much time wasted. Thus, previews are an imperative part of the process.

The final bottleneck that is commonly seen amongst studios, as mentioned previously, is a unified asset browser and reference library. As part of both the previsualization process, the referencing and artist creation process, and the supervisor discussion process, there are a lot of parts of the overall pipeline that can use a unified asset browser and asset library. Without such a component, the pipeline and teams, from artists to CG supervisors to sales executives, will struggle to know what has been done, what the company is good at, and what assets teams can reuse.

Building an asset library is imperative even at the small stage and even more desperate as you scale to over 60+ artists and employees. Why? Because reusing assets saves loads of time as we will discuss later.

Overall, we can see that the VFX workflow centered around a pipeline for every company is an incredibly complex set of technical tooling to increase efficiency with the goal of closing more clients and maintaining artist morale. This is the bread and butter of operational efficiency in VFX. Thus, it is incredibly important for any VFX company to optimize its pipeline as much as it can to prioritize artists and team velocity.

Ironically, it is often the pipeline team that is also the smallest compared to the other teams, and they are often backlogged with a significant number of requests, bug reports, and more. That is why choosing software partners for certain parts of the pipeline can often make sense.

C. The State of Generative AI & AI in the Pipeline

AI is gradually making its way into the world of VFX, although its adoption is still relatively limited. One of the main reasons for this is the challenge of working with high-quality large files that are common in the VFX industry. AI algorithms require significant computational resources and processing power to handle such files efficiently. Additionally, AI models often struggle to adapt and learn from new data or changing scenarios, which is a crucial requirement in the fast-paced and ever-evolving field of VFX oftentimes have the 0.0001% most unique shots that an AI, optimized on the 99.99%, will most likely never see. As a result, AI is sometimes seen as a “junior artist” who lacks the experience, flexibility, and creative problem-solving skills of human artists.

Despite these limitations, there are notable use cases where AI has shown promise in the VFX industry. AI-powered tools have been developed to automate certain repetitive tasks, such as rotoscoping or generating textures and animations (and yet even these are far and few between).

For example, most VFX companies shy away from tooling like Runway ML due to a few things – 1. Core shutdown constraints (most companies are under strict NDAs and “no-internet” policies under certifications such as TPN), thus it’s impossible for them to even take on the additional counterparty risk of using an online tool. 2. It’s incredibly infeasible to upload any sort of 4K footage to an online tool, wasting tons of time just in the transfer process and being significantly disappointed when the results aren’t great. And 3. AI is binary – it either works or it doesn’t. This is perhaps the largest frustration with AI; without the ability to modify the output of the AI (which oftentimes leaves you pixels and masks, and not things that users can feasibly edit in a reasonable time), it is often better to just do it from scratch rather than rely on an AI.

These tools can save valuable time and effort for artists, allowing them to focus on more creative and complex aspects of their work. However, it is important to strike a balance between utilizing AI for efficiency and preserving the artistic vision and expertise of human artists. As the technology continues to advance and overcome its current limitations, AI has the potential to play a more significant role in enhancing workflows and delivering stunning visual effects in the future.

However, what most companies agree on is that AI can seek to automate the most mundane parts of any workflow, and they are most excited about technologies such as automated metadata tagging, auto-organization of files, and AI rotoscoping that is modifiable.

Indeed, the future will never be AI that replaces artists but AI that enhances artists’ workflows.

II. Challenges in Multi-Asset Workflow Management

Manual Organization:

Pre-production involves storyboard animators and software decisions, which then leads to artists working on 3D models, Matte environments, and reference photography, and finally, in post-production, Motion tracking.

A. Importance of Metadata and Labeling in Asset Organization

The most common image file formats used in VFX and rendering are EXR, TGA, TIFF, and PNG. 3D Asset formats used alongside video game animation, films, or manufacturing are STL, OBJ, DAE, etc. Outside of this, media assets also incorporate Audio file formats and categorization of RAW, LOG, and more. In an industry where speed is crucial, artists and studios are forced to create the most efficient workflows while combating common issues of storage capacity, flexibility, and security between software, tools, and teams. Asset organization has never been more important.

Role of metadata in categorizing and retrieving assets

Metadata is a crucial component of data that provides descriptive information about other data. It provides context with details such as the source, type, owner, and relationships to other data sets. In essence, it serves as a label or descriptor for a particular piece of content or asset. In various industries, metadata plays a pivotal role in enhancing the organization, categorization, searchability, and management of vast amounts of data. For instance, in the field of visual effects (VFX), metadata can encompass a range of information, including asset names, creation dates, project details, camera settings, color profiles, and more.

Different types of files, such as VFX assets, audio files, and camera raw file types, can contain specific metadata related to their respective attributes. VFX assets might include information about render settings, layers, and animation parameters. Audio files could hold metadata about the recording date, artist, and even technical aspects like bit rate. In the case of camera raw files, metadata could encompass exposure settings, lens details, and image dimensions. This diverse range of metadata provides a comprehensive view of an asset’s characteristics and origins.

The information contained in metadata is utilized in various ways throughout different stages of content creation and management. In data management, metadata aids in the organization and retrieval of assets by enabling efficient searches based on specific attributes. As content is used, edited, and shared, metadata becomes essential in tracking versions, licensing details, and usage rights. In video asset management (VAM) and digital asset management (DAM) systems, metadata is employed to categorize, tag, and label assets, making them easily searchable and accessible.

For VFX supervisors, 3D artists, video editors, and creators, metadata holds immense value. It allows them to accurately locate assets, such as the precise “sunrise animation, Mogrt file,” amidst a vast repository of content. This accuracy not only saves time but also ensures the correct application of assets, contributing to a seamless workflow. Metadata-driven labeling provides a systematic way to categorize and classify assets, making it easier to organize, manage, and retrieve content. Metadata tagging involves associating descriptive keywords or labels with assets, enabling efficient search queries based on specific criteria.

Labels or tags generated from metadata serve as powerful tools in asset organization. They facilitate the creation of collections based on specific themes, visual tags, projects, or campaigns. This capability aids in maintaining consistency across various projects and allows for easy retrieval of assets for reuse. Moreover, metadata-driven asset organization streamlines the VFX workflows by providing quick access to essential assets, optimizing collaboration, and enhancing overall efficiency.

B. Challenges in maintaining consistent and accurate metadata across projects

The seamless integration of metadata across projects still presents a multifaceted challenge that touches upon file formats, industry standards, collaboration dynamics, and data organization. While metadata serves as the backbone of efficient data management, its effectiveness across different stages of production often falls short of expectations.

One of the most recurring issues arises from the divergence between the metadata generated in common file formats used for rendering, rotoscoping, and video editing and its usefulness or reliability when employed in the context of visual effects (VFX). Often, metadata fails to adequately convey essential details needed for VFX or becomes susceptible to inconsistencies, necessitating time-consuming efforts to reverse engineer information from alternate sources. The variations in how metadata is understood and processed across different stages of production pose significant barriers to interoperability and streamlined workflows.

Major camera and lens manufacturers have offered insights into metadata encoding through SMPTE Registered Disclosure Documents (RDDs). However, despite these disclosures, a comprehensive and standardized approach to metadata remains elusive. Each manufacturer employs unique methodologies, leading to a lack of uniformity in metadata interpretation and translation. The absence of a cohesive standard contributes to inefficiencies in data handling, making it challenging for different systems to effectively communicate and share critical information.

Standardization issues extend beyond technical aspects into broader pipeline and collaboration concerns. In the domain of VFX, establishing standardized practices for pipeline management and the handoff between developers and artists proves challenging due to differing mindsets and skill sets. The intricate interplay between creative artists and technical developers often results in a lack of alignment, making it difficult to establish consistent workflows. The costly nature of data searches and the diverse spectrum of stakeholders—ranging from artists and directors to supervisors and developers—further exacerbate data fragmentation across multiple servers. The lack of uniform data formatting compounds this issue, with little to no standardization between artists and engineers.

These challenges manifest prominently in various sectors of the industry. For major game developers and 3D artists working at game companies, the issue of source control emerges as a significant obstacle. Dealing with duplications, overwriting files, and managing complex asset repositories creates an environment of confusion and inefficiency. Freelance artists and CG generalists might find simple extensions to address these issues, but the magnitude and complexity of data within studios and large companies far surpass these solutions. Manual efforts become the fallback, leading to the neglect of assets, objects, and the missed potential of valuable resources.

The existence of cross-functional teams with varying specialties underscores the need for efficient metadata systems. Organizational divisions are frequently established to cater to distinct disciplines, from the engine team to the narrative team and the 3D/2D asset team. Despite these divisions, the challenge lies in maintaining synchronization and alignment among them. In such an environment, determining the most accurate version of a file or asset becomes a time-consuming ordeal, necessitating traversing through a maze of versions and repositories.

Take, for instance, the case of the engine team, which maintains a certain level of control over asset repositories, providing structure and oversight. This control becomes vital, especially when dealing with the 2D and 3D teams, which can sometimes comprise a large number of individuals. This hierarchical arrangement seeks to prevent chaos amidst a cascade of contributors while still enabling collaboration. However, it also surfaces another complexity – the diversity of expertise levels within these teams.

The industry boasts seasoned professionals with years of experience as well as newcomers fresh out of school, embarking on their creative journey. This juxtaposition is emblematic of the multi-generational, multi-experience landscape that the creative field embodies. The stark contrast in familiarity with tools like GitHub or software like Maya poses challenges in maintaining consistent workflows and metadata practices across the board.

Another layer of intricacy is introduced when delving into optimization for specific engines or packaging assets, where the content of files is sometimes not immediately apparent due to incomprehensible file names.

Even with hierarchical folder structures, effective organization becomes difficult due to inadequate tagging and metadata practices. Inadequate search functionalities further exacerbate the challenge, leaving professionals with limited tools to locate crucial assets efficiently. The absence of robust file history tracking adds to the predicament, further underscoring the need for comprehensive metadata systems.

Across multimedia workflows, the demand for enhanced organization, improved metadata management, and effective search and tagging capabilities is evident. Initiatives to enforce specific namespaces and folder structures can only go so far, as achieving alignment across teams remains a substantial hurdle. The implementation of better metadata practices, more sophisticated search mechanisms, and improved tagging systems holds the potential to alleviate these challenges, fostering collaboration and maximizing the utilization of assets.

III. AI-Driven Solutions for Automated Asset Organization

A. AI-Powered Automated Metadata Generation

Explanation of AI’s role in analyzing and generating metadata

AI plays a pivotal role in automating metadata generation for images and videos. One of the primary applications is in content analysis. Computer vision algorithms can analyze frames from video footage or individual images to automatically generate metadata. AI has the potential to identify and label specific visual elements in a movie production pipeline, such as characters, objects, or scenes. This metadata can be incredibly valuable for VFX artists and editors, as it helps streamline the post-production process. For instance, when adding special effects to a scene, knowing the exact location of characters or props within each frame can be crucial for precise integration. AI can also make generating temporal metadata easier, which is essential in the VFX Industry for tasks like tracking camera movement or identifying the duration of specific visual effects sequences. By analyzing frame changes over time, AI can help generate motion, speed, and timing metadata, providing VFX professionals with critical information.

Semantic understanding is another essential aspect of AI’s role in metadata generation. AI not only recognizes visual elements but also understands their context. For example, it can identify the role of an object within the narrative and generate context-aware metadata. Furthermore, AI can recognize relationships between objects and characters within a scene, leading to more informative metadata that aids VFX artists.

Benefits of accurate and automated metadata for asset discovery

Let’s Consider an Example: Traditional auto-tagging can recognize a car in a movie scene but fails to represent the context. Since AI is capable of semantic understanding, this leads to more informative meta-data such as “high-speed car chase sequence” or “car used as a prop,” which provides a deeper level of context for VFX artists and editors. AI can also identify relationships between objects and characters within a scene. For instance, it can recognize that characters in a scene are related and generate metadata indicating this relationship (Figure 1).

Accurate and automated metadata generation offers several benefits for asset discovery in the VFX industry, including collaboration, asset integration within visual effects sequences, and content organization and archiving. VFX artists, editors, and directors can access consistent and standardized metadata, ensuring a shared understanding of content. This fosters seamless communication and enhances creative collaboration during the post-production phase.

Accurate metadata enables precise asset integration within visual effects sequences. VFX artists can rely on metadata to position objects, characters, and effects accurately within scenes, leading to a more convincing and realistic final product.

Automated metadata generation contributes to content organization and archiving. Assets are tagged, categorized, and enriched automatically, making it easier to maintain a well-organized digital library. This organized structure not only improves asset discovery but also supports long-term content preservation and management.

Accurate and automated metadata generated by AI simply enhances asset discovery in the VFX Industry. It streamlines content management, fosters collaboration, ensures precise asset integration, and promotes effective content organization and archiving. These benefits collectively contribute to the quality of visual effects production in film, television, and animation.

Cost Efficiency and Enhanced Profitability through Automation

Labor Costs in VFX have become exuberant recently, with a median salary for an experienced artist at over $129,000 and an expected job growth rate of 8% over the next 10 years (Bureau of Labor Statistics). Automated metadata generation reduces manual labor through several different avenues, including eliminating manual tagging and streamlining production workflows. In the VFX Industry, one of the most labor-intensive tasks has traditionally been manually tagging and annotating visual elements within images or video frames. For instance, when creating special effects or compositing scenes, VFX professionals often need to meticulously tag objects, characters, or other elements within each frame. This process is time-consuming and prone to entry errors.

AI metadata generation provides efficiency. When creating visual effects for a movie, manually identifying the position and movement of objects within video footage is time-consuming and limits the flexibility of the creative process. AI can identify not only the location of objects but also their interactions within the scene. This automation significantly speeds up the process of integrating visual effects into the original footage. If a VFX artist needs to locate all scenes featuring a particular character in a movie project, they can also perform a targeted search using automated metadata tags. This eliminates the need for time-consuming manual browsing through extensive content libraries.

B. Visual Search and Preview Using AI

Introducing AI-driven visual search capabilities for assets

VFX professionals often need to find elements that match specific visual criteria, such as textures, lighting, or colors, to maintain visual consistency in their projects. AI can analyze these visual characteristics and retrieve assets that closely match the criteria. For instance, if a VFX artist is working on a fantasy film with a specific visual aura and lighting style, Ai can identify assets that exhibit similar lighting and textural attributes, simplifying the process of selecting visual elements that align with the project’s aesthetic.

AI-driven cross-referencing and relationship discovery continue to enhance workflows. VFX artists frequently reuse assets across different scenes and projects. AI can automatically identify relationships between assets. For example, if an artist is working on a VFX sequence involving a specific character, AI can suggest related scenes or objects used in conjunction with that character, ensuring consistency and coherence in the visual effects. This cross-referencing capability accelerates asset discovery and encourages creative reuse, leading to time and cost savings in the production pipeline.

Enabling efficient browsing and previewing of diverse file types

AI-powered video scrubbing previews offer VFX professionals the ability to efficiently assess video content without opening each file while browsing. In the VFX Industry, this means that artists can quickly skim through video footage, identifying specific scenes or shots that require additional work. For instance, a VFX supervisor can use video scrubbing to review sequences and pinpoint scenes where CGI elements or enhancements are most necessary. This not only speeds up the selection process but also ensures that artists focus their efforts on the most relevant portions of the video, optimizing productivity.

AI-driven 3D previews directly within the file explorer are an overhaul to the traditional workflow. These previews enable artists to interact with 3D models and scenes without opening dedicated 3D modeling software. When selecting a character model for a VFX project, an artist can rotate, zoom, and inspect the 3D model within the file explorer to ensure it meets the project’s requirements. This eliminates the need for switching between software, simplifying asset evaluation, and speeding up selection.

AI-driven content similarity analysis, cross-referencing, and relationship discovery, combined with enhanced browsing and previewing capabilities, are powerful tools in the VFX Industry. VFX artists can view high-resolution images, play audio files, or interact with 3D models directly within the file explorer without needing to open separate applications. This compatibility streamlines asset evaluation, promotes creative exploration, and saves time, ultimately enhancing productivity in the VFX workflow.

IV. Enhancing User Experience Through AI Integration

A. Simplifying Workflows with AI-Driven Tools

Introduction to AI-powered tools that simplify complex tasks

AI-powered tools have revolutionized the post-production industry by simplifying complex tasks and enhancing the overall user experience. These tools leverage advanced algorithms and machine learning to automate manual processes, saving time and improving efficiency.

Examples of AI-driven features that enhance user experience

Several AI features are flooding the post-production space, including AI Smart Collections, Tagging and Captioning, Facial Recognition, AI Rotoscoping, Media Upscaling, and Image Generation. These tools speed up repetitive tasks and simplify more complex work.

Managing large collections of objects, images, and footage is a challenge in the VFX and Media industries. AI Smart Collection Tools extract and use EXIF data, rely on visual elements, and use other cues to categorize these assets into topic groups. For example, when adding an animal to a scene, you can open an AI-generated collection of animal-related object files, whether it be a lion, bear, cat, dog, etc. This also extends to animal features, such as eyes, snout, tail, etc.

AI-driven tagging and captioning, as discussed before, is a process in which a large language model (LLM) scans visual elements and attempts to understand relationships within a scene, object, or image. It then automatically tags these elements with descriptive keywords and generates coherent captions to provide context to the visual content. This is done through visual analysis, object recognition, contextual understanding, and continuous learning from large data sets.

Facial recognition tools operate in a similar way as smart collections; however, they simplify the identification of individuals in photos and videos rather than objects and elements. This can be used to track the movement of actors and between scenes, improve character animation, and potentially even automate the task of applying makeup and digital effects to faces.

Rotoscoping is the process of manually tracing objects and figures frame-by-frame. AI-powered rotoscoping allows automatic tracing, creating masks for you through object recognition. This also makes motion tracking much easier and more accurate than traditional tools.

Media upscaling is a critical aspect of enhancing older or lower-quality footage. AI-Upscaling intelligently adds detail while minimizing artifacts. This helps add consistency to the overall work, making it look more polished, especially when the work is shot across different equipment.

Finally, Generative AI opens up more creative possibilities while still speeding up traditional tasks. Removing objects from scenes and images follows an extensive selection, removal, and touchup process in the traditional workspace. Generative AI streamlines the selection process while completely replacing the manual removal and touchup. AI tools allow even inexperienced users to prompt command tasks that remove objects and fill the empty space behind the scenes. This also has opened the door to expanding footage by adding still objects and props. For Example, if all the action in a given sequence is in one area of the frame, the bordering space can be expanded upon, adding volume to the environment and even changing the stage.

B. Seamless Integration with Industry Tools

How an AI platform streamlines interactions between different software and the Benefit of Reducing Manual Data Transfers and Format Conversions

Streamlining interactions between different software platforms can significantly impact productivity and creativity. This is precisely where an AI platform, like Shade, steps in to cut labor costs and boost efficiency.

Accessing Files Between Different Software Platforms:

Traditionally, working with multiple software platforms in VFX and creative industries often meant dealing with file compatibility issues, time-consuming imports, and exports. An AI platform can seamlessly bridge this gap. Here’s how:

Universal File Access: AI-powered platforms can serve as a central repository, allowing you to access your media assets from various software applications. Shade, for instance, lets you access your files from anywhere, be it SMB shares, NFS, S3 storage, and more. This means you’re no longer constrained by the limitations of a single storage format.

Streamlined Data Exchange: An AI platform acts as a universal translator, ensuring that files are converted and transferred seamlessly between different software platforms. This eliminates the hassle of manual transfers, preserving data integrity and saving time.

Previewing Assets Without Opening Multiple Applications:

Traditionally, to view certain file types like HDR, EXR, OBJ, or BLEND, you’d have to open the respective app. Using an AI file explorer that supports file previews this is no longer necessary.

Visual Asset Preview: With AI-driven technologies, you can preview your assets directly within the AI platform. This means you can view images, videos, 3D models, and other media without having to open multiple software applications. This streamlined preview process reduces the risk of errors and speeds up decision-making.

Exporting Splines and Extracting Clips:

Precise control over elements like splines and clips is vital in VFX and creative work. An AI platform can enhance these capabilities:

Exporting Splines: Shade, for instance, allows users to export splines in a single click into Adobe After Affects or Nuke. This feature empowers artists to take their work seamlessly from one software to another, preserving precision and time.

Extracting Clips: AI-driven media management tools can efficiently extract and organize clips from your media assets. This feature is invaluable when working on large-scale projects with extensive footage.

V. Conclusion

A. Recap of the benefits of an AI-driven Workflow

AI-driven tools are poised to revolutionize asset management in the VFX and media industries. These powerful tools automate critical tasks like metadata generation, content analysis, visual search, and previewing, significantly reducing manual labor and saving precious time. The result is improved asset discovery, streamlined workflows, and enhanced collaboration among VFX artists, editors, and directors.

But the impact goes beyond efficiency. Integrating AI with industry-standard tools, such as Shade, simplifies complex tasks and elevates overall productivity within the VFX workflow. As AI technology continues to advance, professionals are strongly encouraged to explore these AI-enhanced workflows. By doing so, they can unlock the full potential of tools like Shade, leading to heightened efficiency, and increased profitability. With such tools flooding the market, taking advantage of AI is not optional, but necessary to maintain a competitive edge in the creative landscape.

Reiteration of how Shade’s AI-powered file explorer addresses industry challenges

Shade’s AI-powered file explorer is a groundbreaking solution that directly addresses several critical challenges in the creative and media industries. Leveraging the power of artificial intelligence, Shade redefines the way professionals manage, access, and utilize their digital assets. Here’s how Shade’s AI-powered file explorer tackles industry challenges:

Content Overload and Organization:

Challenge: The exponential growth of digital content has made it increasingly difficult to organize and manage files effectively. Traditional file structures and manual tagging often fall short.

Solution: Shade’s AI automatically organizes digital assets based on their visual content, eliminating the need for manual tagging and providing accurate and efficient file categorization.

Search and Retrieval Efficiency:

Challenge: Locating specific files within vast media libraries can be time-consuming and frustrating. Keyword-based searches may yield incomplete or irrelevant results.

Solution: Shade’s AI-powered search allows users to find files based on visual elements within the content, making searches more intuitive and comprehensive. This drastically reduces search time.

Metadata Generation:

Challenge: Manually entering metadata is a time-intensive process prone to errors and inconsistencies. Metadata is crucial for asset management and searchability.

Solution: Shade’s AI generates metadata automatically by analyzing the visual and contextual content of files. This ensures accurate and consistent metadata for each asset.

Collaboration and Workflow Efficiency:

Challenge: Collaborative projects often involve multiple software tools and platforms, leading to data transfers, format conversions, and workflow disruptions.

Solution: Shade seamlessly integrates with various software and platforms, facilitating smooth collaboration and reducing the need for manual data transfers.

Rotoscoping and Visual Effects:

Challenge: Rotoscoping, a labor-intensive task in VFX and animation, consumes valuable time and resources.

Solution: Shade’s AI-powered tools automate rotoscoping processes, drastically speeding up the production timeline and ensuring consistent results.

AI-Driven Tagging and Captioning:

Challenge: Manually tagging and captioning visual content is time-consuming and prone to human error.

Solution: Shade’s AI generates descriptive tags and captions automatically, improving content organization and accessibility.

Enhanced Media Quality:

Challenge: Low-resolution media assets can hinder the quality of the final output.

Solution: Shade’s AI includes media upscaling capabilities, enhancing the quality of assets and ensuring consistency in visual content.

Asset Library Management:

Challenge: Managing extensive asset libraries, including archiving and reuse, can be chaotic and inefficient.

Solution: Shade’s AI helps create organized and searchable asset libraries, enabling efficient asset reuse and reference.

AI-Driven Rotoscoping:

Challenge: Manual rotoscoping is time-consuming and prone to inconsistencies.

Solution: Shade’s AI automates the rotoscoping process, significantly reducing production time and maintaining quality.

Future-Ready Innovation:

Challenge: Staying competitive in the creative and media industries requires adapting to new technologies and workflows.

Solution: Shade’s continuous learning and adaptability ensure it remains at the forefront of innovation, empowering professionals with cutting-edge AI tools.

Shade’s AI-powered file explorer represents a transformative leap forward in managing digital assets, streamlining workflows, and enhancing creative processes. By addressing these industry challenges, Shade empowers content creators, VFX artists, and professionals in various creative fields to work more efficiently, creatively, and competitively in a rapidly evolving digital landscape.

B. Call to Action: Exploring Shade as a Solution

How does Shade compare to Competitors?

FeatureShadeGoogle Cloud Vertex AIAzure ML ServiceBynderBrandfolder
Self hosted, no internet
Image / Video Previews
AI Tagging
EXIF Data Extraction
AI Captioning
Search API
Indexing API
Text Search
Custom Models
AI Neural Search
RAW Image Support
R3D, BRAW, Complex Codec Video Support
Natural Language Search
Custom Metadata
Setup Time (under 30 minutes)
AI audio support
AI 3D support
Image sequences (exr/png/etc.)
Project support (Blender, Houdini, Unreal, Photoshop, etc.)
Document formats (.pdf, .ai)
Stock search
Material PBR Support
unpluged / external asset search & preview
EXR Support
EXIF Metdata
Autogenerated Collections
Full list of support here

How Does Shade Compare to Building in House a Solution?

Here is an estimate of costs relative to the different problems Shade has solved:

Vector & Asset Local Database with migrations

Indexing Pipeline, scanning

AI Indexing Exotic videos (i.e. .braw, .r3d)

API Development & Testing

Video and Image Previews

Custom AI Model Training & Finetuning

Model distribution, batching, updating, optional models

RAW Image Support

Stock Search Integrations

Texture Map Parsing and Material Integration

Frontend UI Development, Testing, and Management

Facial Recognition and Search

Network Drive, Path Translation, Protocol Support

Packing Distribution // Run AI anywhere

Offline Asset Support

Audio Support and AI Finetuning

S3 Support and Resyncing

Cost Comparison

Our estimates believe that this entire task amounts to a consistent 15 full-stack developers along with 2-3 experts in machine learning along with a slew of bug fixing and timeline changes, costing up to over $1M in salaries alone per year, let alone storage and maintenance costs, updates, releases and more.

Deployment Next Steps

  1. Connect with creative agencies to set up a local Shade Server
  2. Install Shade on test workstation(s) requiring search
  3. Index asset archives integrate with artist workflow after 1 week
  4. Test search and value metrics – evaluate artist morale, the number of times a user searched, how many assets were reused, and any other gems that can be found.


Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, Special Effects Artists and Animators,
at (visited October 03, 2023).

TaggedAIAsset ManagementVFXVideo Asset ManagementWorkflow

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