One Basis to Animate Them All: Gaussian Blendshape Distillation for Real-Time Avatars
[Widzenie komputerowe] Autorzy: Ramazan Fazylov, Stamatis Lefkimmiatis, Ivan Laptev
3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bottleneck and show that the animation of pretrained avatar models can be closely approximated by a linear combination of identity-independent blendshapes. Building on this finding, we introduce GALA (Gaussian Animation via Linear Approximation), a distillation method that replaces per-frame heavy neural decoding with a shallow coefficient predictor and a linear blend. To improve fidelity and reduce memory requirements, we propose to construct the basis using block-local PCA under a rendering-aware metric and a memory budget. Our method learns a shallow MLP network to predict blendshape coefficients and applies to various animation architectures without retraining original models. We validate GALA by accelerating the inference of three distin…
- Kategoria arxiv
- cs.CV
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- Widzenie komputerowe