EmoTalker：基于扩散模型的情感可编辑说话人脸生成
EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model
AI总结：
本文提出基于扩散模型的 EmoTalker，通过修改去噪过程保留身份，并引入 Emotion Intensity Block 解析细粒度情感，实现高质量、情感可编辑的说话人脸生成。
摘要：
近年来，说话人脸生成领域引起了广泛关注，其中一些方法能够生成逼真模仿人类表情的虚拟人脸。然而，现有方法面临泛化能力有限的问题，尤其是在处理具有挑战性的身份时。此外，用于编辑表情的方法通常局限于单一情感，无法适应复杂情感。为克服这些挑战，本文提出了 EmoTalker，一种基于扩散模型的情感可编辑肖像动画方法。EmoTalker 修改了去噪过程，以确保在推理过程中保留原始肖像的身份。为增强从文本输入中理解情感的能力，引入了 Emotion Intensity Block 来分析从提示中提取的细粒度情感和强度。此外，还利用了一个精心构建的数据集来增强提示中的情感理解。实验表明，EmoTalker 在生成高质量、情感可定制的面部表情方面具有有效性。
英文摘要：
In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating virtual faces that convincingly imitate human expressions. However, existing methods face challenges related to limited generalization, particularly when dealing with challenging identities. Furthermore, methods for editing expressions are often confined to a singular emotion, failing to adapt to intricate emotions. To overcome these challenges, this paper proposes EmoTalker, an emotionally editable portraits animation approach based on the diffusion model. EmoTalker modifies the denoising process to ensure preservation of the original portrait's identity during inference. To enhance emotion comprehension from text input, Emotion Intensity Block is introduced to analyze fine-grained emotions and strengths derived from prompts. Additionally, a crafted dataset is harnessed to enhance emotion comprehension within prompts. Experiments show the effectiveness of EmoTalker in generating high-quality, emotionally customizable facial expressions.
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。
