ReGenHuman: Re-Generating Human Appearances for Realistic Full-Body Video Anonymization

2026-06-12 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, quick

Summary

ReGenHuman is a novel full-body video anonymization pipeline designed to address the limitations of prior techniques that sacrifice realism or temporal coherence. Introduced as the first approach to be simultaneously realistic, temporally consistent, and anonymous by construction, ReGenHuman employs a "regenerate, don't edit" paradigm. It composites 2D pose, segmentation, and monocular depth into two conditioning streams, StructAll and StructHuman, which fine-tune a video-to-video diffusion backbone. This process synthesizes human regions entirely from identity-free structural cues. Evaluations demonstrate that ReGenHuman achieves the best tradeoff across privacy, quality, and utility compared to current baselines, and its anonymized videos remain effective for downstream tasks, including video question answering.

Key takeaway

For Computer Vision Engineers developing privacy-preserving video analytics, ReGenHuman offers a robust solution to anonymize human-centric video data without compromising realism or temporal coherence. You should consider integrating this "regenerate, don't edit" paradigm to ensure identity protection while preserving video utility for downstream tasks like video question answering. This approach allows you to meet privacy requirements effectively.

Key insights

ReGenHuman pioneers realistic, temporally consistent full-body video anonymization by regenerating human appearances from structural cues.

Principles

• Regenerate, don't edit for privacy.
• Identity-free structural cues enable anonymization.
• Balance privacy, quality, and utility.

Method

The method composites 2D pose, segmentation, and monocular depth into StructAll and StructHuman conditioning streams. These fine-tune a video-to-video diffusion backbone to synthesize human regions.

In practice

• Anonymize video datasets for research.
• Protect identities in public video feeds.
• Maintain utility for video analytics.

Topics

• Video Anonymization
• Diffusion Models
• Computer Vision
• Privacy Preservation
• Temporal Consistency
• Human Pose Estimation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.