ScanTalk：基于未注册扫描的3D说话人脸
ScanTalk: 3D Talking Heads from Unregistered Scans
AI总结：
提出ScanTalk框架，利用DiffusionNet架构克服固定拓扑约束，实现任意拓扑结构（含扫描数据）的3D说话人脸动画生成，在真实性与多功能性上媲美现有技术。
摘要：
语音驱动的3D说话人脸生成已成为研究人员关注的重要领域，面临诸多挑战。现有方法受限于为固定拓扑结构的面部制作动画，其中建立了逐点对应关系，并且模型能制作动画的所有身份的点的数量和顺序保持一致。在本研究中，我们提出了ScanTalk，这是一个能够为任意拓扑结构（包括扫描数据）的3D面部制作动画的新框架。我们的方法依赖DiffusionNet架构来克服固定拓扑约束，为更灵活、更逼真的3D动画提供了有前景的途径。通过利用DiffusionNet的能力，ScanTalk不仅能适应多样的面部结构，还能在处理扫描数据时保持保真度，从而增强生成的3D说话人脸的真实性和多功能性。通过与最先进方法的全面比较，我们验证了该方法的有效性，证明其能够生成可与现有技术相媲美的逼真说话人脸。虽然我们的主要目标是开发不受拓扑约束的通用方法，但所有最先进的方法都受到这种限制。用于复现结果的代码和预训练模型可在 https://github.com/miccunifi/ScanTalk 获取。
英文摘要：
Speech-driven 3D talking heads generation has emerged as a significant area of interest among researchers, presenting numerous challenges. Existing methods are constrained by animating faces with fixed topologies, wherein point-wise correspondence is established, and the number and order of points remains consistent across all identities the model can animate. In this work, we present \textbf{ScanTalk}, a novel framework capable of animating 3D faces in arbitrary topologies including scanned data. Our approach relies on the DiffusionNet architecture to overcome the fixed topology constraint, offering promising avenues for more flexible and realistic 3D animations. By leveraging the power of DiffusionNet, ScanTalk not only adapts to diverse facial structures but also maintains fidelity when dealing with scanned data, thereby enhancing the authenticity and versatility of generated 3D talking heads. Through comprehensive comparisons with state-of-the-art methods, we validate the efficacy of our approach, demonstrating its capacity to generate realistic talking heads comparable to existing techniques. While our primary objective is to develop a generic method free from topological constraints, all state-of-the-art methodologies are bound by such limitations. Code for reproducing our results, and the pre-trained model are available at https://github.com/miccunifi/ScanTalk .
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。
