FiCA：基于单张肖像图像的前馈即时高斯编解码器化身
FiCA: Feed-forward instant Gaussian Codec Avatars from a Single Portrait Image
AI总结：
提出FiCA，一种结合人类视觉基础模型和扩散模型的前馈管道，从单张图像生成逼真、可驱动的3D高斯化身，无需测试时优化。
摘要：
我们介绍了FiCA，一种前馈、即时的Gaussian Codec Avatar生成管道，可从单张肖像图像创建逼真的化身。由于可用于准确推断人类头部3D外观和几何的视觉信息有限，仅从单张图像生成逼真且可驱动的化身极具挑战性。为此，我们开发了一种新颖系统，将人类中心视觉基础模型与扩散模型相结合。该系统旨在充分利用部分视觉观察来生成逼真的人类化身。我们提出的扩散模型学习从这些部分观察到完整且真实的3D网格重建的生成映射。此外，我们引入了一个前馈网格细化网络，增强了生成化身的保真度和身份保持，无需针对特定人物的测试时优化。通过利用将生成网格解码为一组3D高斯的通用先验模型，我们生成了一个逼真的3D高斯化身，能够实时驱动以呈现新颖表情。我们的实验表明，我们前馈方法生成的化身忠实代表了多样化的身份，并超越了近期竞争方法生成的化身的视觉质量。
英文摘要：
We introduce FiCA, a Feed-forward, instant Gaussian Codec Avatar generation pipeline that creates lifelike avatars from a single portrait image. Generating a photorealistic and drivable avatar from just a single image is significantly challenging due to the limited visual information available to accurately infer the 3D appearance and geometry of human heads. To address this, we develop a novel system that combines human-centric vision foundation models with a diffusion model. This system is designed to fully exploit partial visual observations to generate lifelike human avatars. Our proposed diffusion model learns a generative mapping from these partial observations to complete and authentic 3D mesh reconstruction. Additionally, we introduce a feed-forward mesh refinement network that enhances the fidelity and identity preservation of the generated avatars, eliminating the need for person-specific test-time optimization. By leveraging a universal prior model that decodes a generated mesh into a set of 3D Gaussians, we generate a photorealistic 3D Gaussian avatar, capable of being driven with novel expressions in real-time. Our experiments demonstrate that the avatars generated by our feed-forward approach faithfully represent diverse identities and surpass the visual quality of avatars produced by recent competing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
