Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915736700583936 |
|---|---|
| author | Egin, Anil Tangherloni, Andrea Dantcheva, Antitza |
| author_facet | Egin, Anil Tangherloni, Andrea Dantcheva, Antitza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11635 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos Egin, Anil Tangherloni, Andrea Dantcheva, Antitza Computer Vision and Pattern Recognition 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. |
| title | Now You See Me, Now You Don't: A Unified Framework for Expression Consistent Anonymization in Talking Head Videos |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.11635 |