发表机构
Mohamed bin Zayed University of Artificial Intelligence; MWS AI（穆罕默德·本·扎耶德人工智能大学; MWS AI公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D高斯化身实时动画的神经推理瓶颈，提出GALA蒸馏方法，用线性混合近似动画，显著降低CPU成本并保持质量，支持移动端60fps。
AI 中文摘要
3D高斯化身支持快速渲染，然而，其实时动画常常受到昂贵神经推理的挑战。我们解决了这一瓶颈，并表明预训练化身模型的动画可以通过身份无关的混合形状的线性组合来紧密近似。基于这一发现，我们引入了GALA（通过线性近似的高斯动画），一种蒸馏方法，用浅层系数预测器和线性混合替代每帧的重神经解码。为了提高保真度并减少内存需求，我们提出在渲染感知度量和内存预算下使用块局部PCA构建基元。我们的方法学习一个浅层MLP网络来预测混合形状系数，并适用于各种动画架构，无需重新训练原始模型。我们通过加速三个不同化身模型的推理来验证GALA，用于面部表情和带衣物动态的全身体的三维动画。在这些模型中，我们的蒸馏泛化到未见的身份，并将CPU动画成本降低多达三个数量级，同时保留大部分渲染质量。我们方法的优秀结果证实了学习到的化身表示的共享线性结构，并能够在移动设备上以高达60fps的帧率实现高效且准确的动画。项目页面：此https URL
英文摘要
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 distinct avatar models for 3D animation of facial expressions and full-bodies with clothing dynamics. Across these models, our distillation generalizes to held-out identities and reduces CPU animation cost by up to three orders of magnitude while preserving most of the rendering quality. Excellent results of our method confirm the shared linear structure of learned avatar representations and enable highly efficient and accurate animation at frame rates reaching up to 60fps on mobile devices. Project page: https://ramazan793.github.io/gala/
