保护隐私的同时保留表情与动作，让换脸视频看不出原样却仍自然。
Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos
- 用扩散模型结合属性识别与动态表情迁移，精准替换人脸。
- 在三个数据集上验证，既隐藏身份又保持表情和动作一致。
- 适合需要隐私保护的视频分析场景，如行为识别与追踪。
人脸视频匿名化旨在保护隐私的同时支持计算机视觉下游任务，如表情识别、人物追踪和动作识别。本文提出一种名为 Anon-NET 的统一框架，可高效去标识人脸视频，同时保留原始视频中的年龄、性别、种族、姿态和表情信息。具体而言，通过基于扩散的生成模型，在高层属性识别与运动感知的表情迁移引导下进行人脸修补；随后采用视频驱动动画技术，将去标识后的人脸与原始视频结合以生成动画结果。在包含多样面部动态的 VoxCeleb2、CelebV-HQ 与 HDTF 数据集上的大量实验表明，AnonNET 能有效隐藏身份，同时保持视觉真实性和时间一致性。代码将公开发布。
原文摘要 · Abstract (English)
Face video anonymization is aimed at privacy preservation while allowing for the analysis of videos in a number of computer vision downstream tasks such as expression recognition, people tracking, and action recognition. We propose here a novel unified framework referred to as Anon-NET, streamlined to de-identify facial videos, while preserving age, gender, race, pose, and expression of the original video. Specifically, we inpaint faces by a diffusion-based generative model guided by high-level attribute recognition and motion-aware expression transfer. We then animate deidentified faces by video-driven animation, which accepts the de-identified face and the original video as input. Extensive experiments on the datasets VoxCeleb2, CelebV-HQ, and HDTF, which include diverse facial dynamics, demonstrate the effectiveness of AnonNET in obfuscating identity while retaining visual realism and temporal consistency. The code of AnonNet will be publicly released.
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