Real-Time AI Rendering
Revolutionizing production workflows with instant AI-powered visualization of motion capture data
Revolutionizing production workflows with instant AI-powered visualization of motion capture data
Due to the bleeding-edge nature of this project and its ongoing development, portions of this case study have been temporarily redacted. For complete information or collaboration opportunities, please contact me directly.
The AI Render Base (ARB) is a groundbreaking workflow I developed that brings unprecedented flexibility to previsualization and fast-paced production environments. Building on my previous AI rendering experiments, ARB takes a critical leap forward by enabling real-time processing of motion capture data to render high-quality images almost instantly for any scenario.
In traditional workflows, rendering a scene in Unreal Engine involves multiple time-consuming steps: designing environments, building and rigging characters, and importing elements into scenes. ARB dramatically simplifies this process, allowing creatives to switch out any scene element by simply altering a text prompt. Characters, backgrounds, lighting—all can be changed in real-time while maintaining visual consistency.
This innovation bridges the gap between multi-million dollar production systems like those used for films such as Avatar and what's accessible to smaller studios or independent creators. By combining motion capture technology with optimized AI rendering, ARB enables more accessible, efficient, and flexible creative pipelines for both rapid previsualization and full-scale production.
Create a real-time AI rendering system for motion capture data that offers flexibility, speed, and quality comparable to high-end production systems.
3 months of intensive development (January - April 2024), building on previous AI rendering research.
Lead Developer, Technical Director, and Researcher, conceptualizing the approach and implementing the complete pipeline.
Motion Capture, Giant, Unreal Engine, ComfyUI,
, AI image generation models, OpenPose, Blender, GPU optimization.The traditional production workflow presents numerous obstacles to rapid iteration and creative experimentation:
ARB overcomes these challenges through a novel approach that combines motion capture, 3D animation, and optimized AI rendering:
Early demonstration of the ARB system using
to bypass preprocessingFinal version demonstration achieving optimal speeds and quality
Friend having fun with the early
prototype real-time system.Early prototype demonstrating the system works for rendering normal people
Developing ARB required systematic innovation and problem-solving across multiple technical domains. The journey from concept to functioning system involved several critical breakthroughs and iterations.
The project began as an extension of my previous AI rendering work. After creating a photorealistic rendering workflow for AI, the next logical step was making it work in real-time. Having access to a motion capture room and Unreal Engine, I decided to combine these with ComfyUI and the AI models I had been using.
Initial tests explored multiple pathways, with the first attempts using camera input and ControlNet for pose detection. While this worked conceptually, it introduced significant delays due to the preprocessing requirements of ControlNet, making real-time application impractical.
Early proof-of-concept demonstration showing the initial approach. Demonstrates easy replacement of characters using prompt. (hint: this video goes into detail on prompting for consistent characters)
The key innovation came when I realized I could bypass the slow ControlNet preprocessing by creating a direct pipeline from motion capture to AI rendering. As I noted in my development diary: "How do I speed up pose? That's when I realized I can rig a model that looks like the system that open pose uses and connect that model to the mocap room. That way I am directly streaming the motion of the character to my workflow in real time without having the delay of the control net pre-processor!"
This approach involved rigging a 3D model in Blender that matched OpenPose's skeletal structure, bringing that into Unreal Engine, and then connecting it to the motion capture system. This eliminated the need for pose detection entirely, as the pose data was already available in the exact format needed.
Left: OpenPose skeleton controlled by mocap; Right: Actor calibrating movements for the mocap stage
With the core concept working, my focus shifted to optimization. The initial implementation was promising but still too slow for true real-time application. I documented this struggle in my notes: "I tried using a camera and canny control net which is a common workflow for this type of scenario, but the biggest issue was that it was very slow, especially because of the control net. So my R&D focused on speeding it up."
One of the first times I got the system working with OpenPose
Through iterative testing and implementation of specialized techniques like
and , I was able to dramatically improve performance. Additional optimizations included:Testing the improved performance with a human subject
A critical aspect of ARB's value proposition is the ability to easily switch characters while maintaining the same motion data. To validate this capability, I conducted extensive testing with various character types, from realistic humans to stylized creatures.
This testing revealed the system's remarkable flexibility—the same motion capture data could drive completely different characters simply by changing text prompts. From humans to humanoid cats and lions, each character maintained appropriate motion while taking on the visual characteristics specified in the prompts.
Character versatility testing: Lion humanoid (left) vs. Humanoid Cat (right) using identical motion data
The final development phase involved integrating the system with professional-grade motion capture equipment. Working at USC's mocap lab, I was able to use the same Giant software employed in productions like Avatar, bringing that data into my ARB pipeline.
This integration validated that ARB could work with industry-standard tools and wasn't limited to consumer-grade motion capture. It demonstrated the system's potential for professional production environments while maintaining its accessibility for smaller studios.
Left: Actor suited up for motion capture; Right: Setting up the skeleton for mocap integration
The ARB workflow consists of several interconnected components working in harmony:
Achieving real-time performance required several critical optimizations:
Result: Processing speed improved from several seconds per frame to just 0.08 seconds per image on a 4090 GPU — equivalent to 12.5 frames per second.
One of ARB's most powerful features is the ability to change visual elements through simple text prompts:
// Example prompt structure for character rendering
{
"character": "humanoid lion with golden mane, photorealistic",
"environment": "futuristic laboratory with blue lights",
"lighting": "dramatic side lighting with rim light",
"style": "cinematic, detailed, 8k, movie quality"
}
This text-based control eliminates the need for extensive 3D modeling and environment creation typically required in game engines. A single prompt change can completely transform the scene while maintaining the same motion data.
The AI Render Base represents a significant advancement in real-time visualization technology, creating new possibilities for both previsualization and production workflows.
Beyond the quantitative metrics, ARB delivers several transformative benefits:
"The system demonstrates remarkable potential for transforming production workflows. This technology could fundamentally change how smaller studios approach visualization."
— USC Faculty member after system demonstration
Friend reacting to the early prototype, showing genuine enthusiasm for the technology
This video provides a comprehensive demonstration of the ARB system in action:
ARB's versatility and performance are showcased throughout this case study.
ARB enables directors and cinematographers to rapidly visualize scenes with different characters, environments, and lighting without the time-consuming setup of traditional previs. This accelerates creative exploration and decision-making.
Game developers can quickly prototype character animations and environments, testing multiple visual styles without committing extensive art resources early in development.
ARB offers an accessible entry point for smaller studios looking to implement virtual production techniques, allowing them to visualize digital environments in real-time with actors.
Character designers can rapidly iterate on visual styles while maintaining consistent performance, testing how different character designs feel in motion before committing to full 3D modeling.
While ARB represents a significant advancement in real-time visualization technology, several enhancement opportunities remain for future development:
Further optimization could push the system beyond 24fps to 30fps or even 60fps, enabling true real-time interactive applications beyond visualization.
Expanding the system to handle multiple characters simultaneously would enable more complex scene visualization with character interactions.
Moving computation to cloud-based GPUs could democratize access further, allowing creators without high-end hardware to utilize the technology.
Developing more granular control over environmental elements like specific props, weather effects, and time of day would expand creative possibilities.
The AI Render Base represents a significant leap forward in democratizing high-end production visualization, transforming what was once accessible only to major studios into a capability available to a much wider range of creators. By combining optimization techniques, innovative motion capture integration, and text-based scene control, ARB delivers unprecedented flexibility and speed for visualizing creative concepts.
While currently functioning as a proof of concept, the enthusiastic reception from faculty and peers validates the transformative potential of this approach. As AI rendering technologies continue to advance, systems like ARB will likely become standard tools in the creative production pipeline, enabling faster iteration, more experimentation, and ultimately more innovative visual storytelling.
This project demonstrates how thoughtful application of emerging technologies can solve long-standing workflow challenges in creative industries, making sophisticated production capabilities more accessible and democratizing the tools of visual storytelling.