Diffused Heads：扩散模型在说话人脸生成上击败GAN
Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation
AI总结：
提出仅需单张身份图像和音频序列的自回归扩散模型，能生成逼真说话人头视频并保留背景，在两个数据集上达到最先进水平。
摘要：
说话人脸生成在历史上一直难以在没有额外参考视频指导的情况下产生头部运动和自然的面部表情。近期基于扩散的生成模型的发展允许更真实和稳定的数据合成，其在图像和视频生成上的性能已经超越了其他生成模型。在这项工作中，我们提出了一种自回归扩散模型，仅需一张身份图像和音频序列即可生成逼真说话人头部的视频。我们的解决方案能够幻觉出头部运动、面部表情（如眨眼），并保留给定的背景。我们在两个不同的数据集上评估了我们的模型，在两者上均取得了最先进的结果。
英文摘要：
Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic and stable data synthesis and their performance on image and video generation has surpassed that of other generative models. In this work, we present an autoregressive diffusion model that requires only one identity image and audio sequence to generate a video of a realistic talking human head. Our solution is capable of hallucinating head movements, facial expressions, such as blinks, and preserving a given background. We evaluate our model on two different datasets, achieving state-of-the-art results on both of them.
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。
