Better map 2D image points of people to 3D surfaces using novel loss function with UV R-CNN
Better map 2D image points of people to 3D surfaces using novel loss function with UV R-CNN
UV R-CNN: Stable and Efficient Dense Human Pose Estimation
arXiv paper abstract https://arxiv.org/abs/2211.02337
arXiv PDF paper https://arxiv.org/pdf/2211.02337.pdf
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.
… 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, … propose a brand new architecture, named UV R-CNN.
… 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.
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