论文标题
UV R-CNN：稳定而高效的人姿势估计
UV R-CNN: Stable and Efficient Dense Human Pose Estimation
论文作者
论文摘要
密集的姿势估计是实例级人类分析的密集3D预测任务，旨在将人类像素从RGB图像映射到人体的3D表面。由于大量的表面点回归，与其他基于区域的人类实例分析任务相比，训练过程似乎很容易崩溃。通过分析现有密集姿势估计模型的损失公式，我们引入了一种新颖的点回归损失函数，名为密集点}损失以稳定训练进度，以及一种新的平衡减肥策略来处理多任务损失。有了上述新颖性，我们提出了一个全新的建筑，名为UV R-CNN。如果没有其他任务的辅助监督和外部知识，则UV R-CNN可以在密集的姿势模型培训进度中处理许多复杂的问题，从而在密集的coco-coco验证中，在resnet-50-fppn-fplepn peatuctor plemutiations中，在coco-coco验证的范围内实现了66.1％$ ap_ {gps} $和66.1％$ ap_ {gpsm} $。
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% $AP_{gps}$ and 66.1% $AP_{gpsm}$ on the DensePose-COCO validation subset with ResNet-50-FPN feature extractor, competitive among the state-of-the-art dense human pose estimation methods.
