Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos

• URL: http://arxiv.org/abs/2601.11635v1
• Date: Wed, 14 Jan 2026 09:42:44 GMT
• Title: Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos
• Authors: Anil Egin, Andrea Tangherloni, Antitza Dantcheva,
• Abstract summary: Face video anonymization is aimed at privacy preservation while allowing for the analysis of videos in a number of computer vision downstream tasks.<n>We propose here a novel unified framework referred to as Anon-NET, streamlined to de-identify facial videos.<n>We inpaint faces by a diffusion-based generative model guided by high-level recognition and motion-aware attribute expression transfer.
• Score: 8.859607428705846
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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