mlsanityData to 14 Oct 2026

UV R-CNN: Stable and Efficient Dense Human Pose Estimation

arXiv 2211.02337 ↗First posted 4 Nov 2022cs.CVcited by 0

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Abstract (arXiv)

Dense pose estimation is a dense 3D prediction task for instance-level human analysis, aiming to map human pixels from an RGB image to a 3D surface of the human body. Due to a large amount of surface point regression, the training process appears to be easy to collapse compared to other region-based human instance analyzing tasks. By analyzing the loss formulation of the existing dense pose estimation model, we introduce a novel point regression loss function, named Dense Points loss to stable the training progress, and a new balanced loss weighting strategy to handle the multi-task losses. With the above novelties, we propose a brand new architecture, named UV R-CNN. Without auxiliary supervision and external knowledge from other tasks, UV R-CNN can handle many complicated issues in dense pose model training progress, achieving 65.0% APgps and 66.1% APgpsm on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 57 of its 81 references that are in the map. First posted on arXiv 4 Nov 2022.

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References · 57

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38 more
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