One Basis to Animate Them All: Gaussian Blendshape Distillation for Real-Time Avatars
Ramazan Fazylov, Stamatis Lefkimmiatis, Ivan Laptev
arXiv:2610.02207v1Today’s standout paper is One Basis to Animate Them All. The problem it tackles is a practical bottleneck in modern 3D avatars: they can look great, but animating them in real time often depends on heavy neural networks that are too slow for mobile or interactive use. The authors found that many pretrained avatar models, despite their complexity, can be closely approximated by a simple linear combination of shared blendshapes. Based on that insight, they propose GALA, a distillation method that replaces expensive per-frame neural decoding with a lightweight network that predicts a few coefficients, followed by a fast linear blend. They also use a rendering-aware PCA strategy to build a compact basis under a memory budget. The result is impressive: the method speeds up animation by up to a thousand times on the CPU while keeping most of the visual quality, and it works across different avatar systems without retraining them from scratch. That makes high-quality, real-time avatars much more practical.
Also spotted that day
Previous daily papers