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arXiv Computer Vision By Shuang Liang, Jing He, Lejun Liao, Chuanmeizhi Wang, Guo Zhang, Yingcong Chen, Yuan Yuan

SDPose: Exploiting Diffusion Priors for Out-of-Domain and Robust Pose Estimation

Read the original on arXiv Computer Vision →

SDPose leverages pre‑trained diffusion models, specifically the Stable Diffusion U‑Net, to extract robust latent features for human pose estimation. By identifying the most effective upsampling levels, fusing these features with a lightweight aggregation module, and jointly training with keypoint heatmap supervision and a latent reconstruction loss, SDPose achieves strong cross‑domain generalization. The authors introduce the COCO‑OOD benchmark, comprising style‑transferred and corrupted subsets, to evaluate robustness, and demonstrate that SDPose matches Sapiens on COCO while outperforming competitors in zero‑shot cross‑domain scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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