PCHMR: Empowering and Benchmarking Human Mesh Recovery in Privacy-Constrained Real-World Settings
Fine-tuning directly on user-side data is an effective way to improve the performance of widely adopted data-driven human mesh recovery (HMR) models. However, doing so for existing HMR methods often assumes aggregating user-side data from real-world HMR-deployed devices to a central training server, posing a significant privacy risk as sensitive human images are transmitted. How can human mesh recovery be evaluated and improved in privacy-constrained real-world settings? We study this question through a benchmark in which state-of-the-art HMR models are trained with federated learning and a secure-aggregation variant, neither of which centralizes raw images, and evaluated across a wide ra...
| Authors | Zeyu Cao, Qingxuan Wu, Zhiyang Dou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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