Gaussian Splatting vs. NeRF
In the legacy pipeline, 3D scanning required photogrammetry—a brittle process that often failed on thin surfaces or shiny materials. We've solved this with the dual approach of NeRFs (Neural Radiance Fields) and 3DGS (3D Gaussian Splatting). While NeRFs excel at high-fidelity light interaction, 3DGS is our preferred stack for real-time production due to its sub-millisecond rasterization speeds.
| Feature | NeRF (Neural Radiance Fields) | 3DGS (3D Gaussian Splatting) |
|---|---|---|
| Rendering Speed | Slow (seconds/frame) | Real-time (144fps+) |
| Geometry Type | Volumetric Density Field | Anisotropic 3D Gaussians |
| Training Time | Hours to Days | Minutes |
| VRAM Usage | Low to Moderate | High (Scale-dependent) |
Our recent benchmarks on 4D Gaussian Splatting show that we can now reconstruct dynamic human performances with temporal consistency that exceeds traditional motion capture. We're effectively bypassing the "rigging" phase by directly rendering the neural field.
The Move to Move.ai & MetaHuman Animator
Manual character rigging is a bug. We've optimized this by integrating **Move.ai** and **MetaHuman Animator** into a single-pass workflow. By using high-density neural face rigging (NFR) research, we've enabled physics-based facial meshes that handle contact (eyeballs to lids, teeth to lips) without manual weight painting.
- Audio-Driven Animation: 2025 systems now generate plausible head translation/rotation directly from vocal frequency analysis.
- Dynamic Asset Fitting: New parametric body systems adapt clothing to 3DGS-scanned bodies in real-time, eliminating the "clipping" issues that plagued legacy character design.
Scaling Environments
Why build one set when you can deploy an infinite procedural reality stack? We use Diffusion Transformer architectures to 'grow' environments based on narrative seeds. This isn't just random terrain; it's context-aware architecture that follows the logic of the scene.