Human pose regression by combining indirect part detection and contextual information

Diogo C. Luvizon, Hedi Tabia, David Picard

Abstract: Highlights • A new human pose regression method from RGB images. • The proposed soft-argmax operation. • Contextual aggregation for refining predictions. • State-of-the-art results on important 2D pose estimation benchmarks among regression methods. Abstract In this paper, we tackle the problem of human pose estimation from still images, which is a very active topic, specially due to its several applications, from image annotation to human-machine interface. We use the soft-argmax function to convert feature maps directly to body joint coordinates, resulting in a fully differentiable framework. Our method is able to learn heat maps representations indirectly, without additional steps of artificial ground truth generation. Consequently, contextual information can be included to the pose predictions in a seamless way. We evaluated our method on two challenging datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets, reaching the best performance among all the existing regression methods. Source code available at: https://github.com/dluvizon/pose-regression .