PADS: Plug-and-Play 3D Human Pose Analysis via Diffusion Generative Modeling


Haorui Ji (Australian National University), Hongdong Li (Australian National University)
The 35th British Machine Vision Conference
PDFPoster

Abstract

Diffusion models have demonstrated impressive capabilities in modeling complex data distributions and are increasingly applied in various generative tasks. In this work, we propose Pose Analysis by Diffusion Synthesis (PADS), a unified generative modeling framework for 3D human pose analysis. PADS first learns a task-agnostic 3D pose prior via unconditional diffusion synthesis and then performs training-free adaptation to a wide range of pose analysis tasks, including 3D pose estimation, denoising, completion, etc., through a posterior sampling scheme. By formulating each task as an inverse problem with a known forward operator, PADS injects task-specific constraints during inference while keeping the pose prior fixed. This plug-and-play framework removes the need for task-specific supervision or retraining, offering flexibility and scalability across diverse conditions. Extensive experiments on different benchmarks showcase the superior performance against both learning-based and optimization-based baselines, demonstrating the effectiveness and generalization capability of our method.

Citation

@inproceedings{Ji_2025_BMVC,
author    = {Haorui Ji and Hongdong Li},
title     = {PADS: Plug-and-Play 3D Human Pose Analysis via Diffusion Generative Modeling},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_718/paper.pdf}
}


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