上下文感知的说话人脸视频生成
Context-aware Talking Face Video Generation
AI总结：
针对多人互动场景，本文提出以面部关键点为控制信号的两阶段跨模态可控视频生成流程，并通过3D视频扩散模型实现上下文一致的说话人脸生成。
摘要：
本文考虑了说话人脸视频生成的一个新颖且实用的场景。具体而言，我们关注涉及多人互动的场景，其中存在观众或周围环境等说话上下文。在这些情况下，视频生成应当考虑上下文，以便生成与驱动音频自然对齐、并在空间上与上下文连贯的视频内容。为实现这一目标，我们提出了一个两阶段、跨模态且可控的视频生成流程，采用面部关键点作为显式且紧凑的控制信号，以连接驱动音频、说话上下文和生成的视频。在该流程内部，我们设计了一个3D视频扩散模型，能够高效控制空间条件（关键点和上下文视频）以及音频条件，从而实现时间连贯的生成。实验结果验证了所提方法在音视频同步、视频保真度和帧一致性方面相较其他基线方法的优势。
英文摘要：
In this paper, we consider a novel and practical case for talking face video generation. Specifically, we focus on the scenarios involving multi-people interactions, where the talking context, such as audience or surroundings, is present. In these situations, the video generation should take the context into consideration in order to generate video content naturally aligned with driving audios and spatially coherent to the context. To achieve this, we provide a two-stage and cross-modal controllable video generation pipeline, taking facial landmarks as an explicit and compact control signal to bridge the driving audio, talking context and generated videos. Inside this pipeline, we devise a 3D video diffusion model, allowing for efficient contort of both spatial conditions (landmarks and context video), as well as audio condition for temporally coherent generation. The experimental results verify the advantage of the proposed method over other baselines in terms of audio-video synchronization, video fidelity and frame consistency.
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。
