Full-body anonymization

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (7 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy vs. Performance
  24 Oct 2024 1 repository listed
  We evaluated the approaches on publicly available privacy and VAD data sets to examine the strengths and weaknesses of the different anonymization techniques and highlight the promising efficacy of our approach.
• Context-Aware Full Body Anonymization using Text-to-Image Diffusion Models
  11 Oct 2024 1 repository listed
  In order to protect people's privacy whilst keeping important features in the dataset, it is important to replace the full body of a person with a highly detailed anonymized one.
• DeepPrivacy2: Towards Realistic Full-Body Anonymization
  17 Nov 2022 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures.
• Realistic Full-Body Anonymization with Surface-Guided GANs
  6 Jan 2022 1 repository listed
  Recent work on image anonymization has shown that generative adversarial networks (GANs) can generate near-photorealistic faces to anonymize individuals.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.