AnimateMe：基于扩散模型的4D面部表情
AnimateMe: 4D Facial Expressions via Diffusion Models
AI总结：
本文提出AnimateMe方法，将图神经网络用作去噪扩散模型直接在网格空间生成3D面部表情，并引入一致性噪声采样确保时序连贯性，实现了可控且高保真的4D面部动画及带纹理表情生成。
摘要：
近年来，照片级真实3D虚拟人重建与生成领域备受关注；然而，为此类虚拟人生成动画仍具挑战。扩散模型的最新进展显著提升了生成模型在2D动画中的能力。在本研究中，我们直接在3D领域利用这些模型，以实现可控且高保真的4D面部动画。通过整合扩散过程与几何深度学习的优势，我们以新颖的方式将图神经网络（GNNs）用作去噪扩散模型，将扩散过程直接在网格空间上构建，从而实现3D面部表情的生成。这使得通过基于网格扩散的模型生成面部形变成为可能。此外，为确保动画的时序连贯性，我们提出了一种一致性噪声采样方法。在一系列定量和定性实验中，我们展示了所提方法通过生成高保真极端表情，在4D表情合成方面优于先前工作。进一步地，我们将该方法应用于带纹理的4D面部表情生成，实现了一个简单直接的扩展，即在一个大规模带纹理4D面部表情数据库上进行训练。
英文摘要：
The field of photorealistic 3D avatar reconstruction and generation has garnered significant attention in recent years; however, animating such avatars remains challenging. Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation. In this work, we directly utilize these models within the 3D domain to achieve controllable and high-fidelity 4D facial animation. By integrating the strengths of diffusion processes and geometric deep learning, we employ Graph Neural Networks (GNNs) as denoising diffusion models in a novel approach, formulating the diffusion process directly on the mesh space and enabling the generation of 3D facial expressions. This facilitates the generation of facial deformations through a mesh-diffusion-based model. Additionally, to ensure temporal coherence in our animations, we propose a consistent noise sampling method. Under a series of both quantitative and qualitative experiments, we showcase that the proposed method outperforms prior work in 4D expression synthesis by generating high-fidelity extreme expressions. Furthermore, we applied our method to textured 4D facial expression generation, implementing a straightforward extension that involves training on a large-scale textured 4D facial expression database.
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