GeoDiff4D: 基于几何的扩散模型用于4D头像重建
GeoDiff4D: Geometry-Aware Diffusion for 4D Head Avatar Reconstruction
AI总结：
GeoDiff4D通过几何感知扩散模型，结合表面法线和表情编码器，实现了高保真的4D头像重建，提升了视觉质量和跨身份泛化能力。
摘要：
从单张肖像图像重建出逼真且可动画化的4D头像仍然是计算机视觉中的基本挑战。尽管扩散模型在头像重建的图像和视频生成中取得了显著进展，但现有方法主要依赖2D先验，难以实现一致的3D几何。我们提出了一种新的框架，利用基于几何的扩散学习强几何先验，以实现高保真的头像重建。我们的方法联合合成肖像图像和相应的表面法线，同时一个无姿态表情编码器捕捉隐式表情表示。合成的图像和表情潜在特征都被纳入基于3D高斯的头像中，使逼真渲染和准确几何成为可能。大量实验表明，我们的方法在视觉质量、表情保真度和跨身份泛化方面显著优于最先进的方法，同时支持实时渲染。
英文摘要：
Reconstructing photorealistic and animatable 4D head avatars from a single portrait image remains a fundamental challenge in computer vision. While diffusion models have enabled remarkable progress in image and video generation for avatar reconstruction, existing methods primarily rely on 2D priors and struggle to achieve consistent 3D geometry. We propose a novel framework that leverages geometry-aware diffusion to learn strong geometry priors for high-fidelity head avatar reconstruction. Our approach jointly synthesizes portrait images and corresponding surface normals, while a pose-free expression encoder captures implicit expression representations. Both synthesized images and expression latents are incorporated into 3D Gaussian-based avatars, enabling photorealistic rendering with accurate geometry. Extensive experiments demonstrate that our method substantially outperforms state-of-the-art approaches in visual quality, expression fidelity, and cross-identity generalization, while supporting real-time rendering.
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