Hierarchical Generation of Human Pose With Part-Based Layer Representation.

Human pose transfer has been becoming one of the emerging research topics in recent years. However, state-of-the-art results are still far from satisfactory. One main reason is that these end-to-end methods are often blindly trained without the semantic understanding of its content. In this paper, we propose a novel method for human pose transfer with consideration of the semantic part-based representation of a human. In particular, we propose to segment the human body into multiple parts, and each of them represents a semantic region of a human. With the proposed part-based layer generators, a high-quality result is guaranteed for each local semantic region. We design a three-stage hierarchical framework to fuse local representations into the final result in a coarse-to-fine manner, which provides adaptive attention for global consistency and local details, respectively. Via exploiting spatial guidance from 3D human model through the framework, our method can naturally handle the ambiguity of self-occlusions which always causes artifacts in previous methods. With semantic-aware and spatial-aware representations, our method outperforms previous approaches quantitatively and qualitatively in better handling self-occlusions, fine detail preservation/synthesis and a higher resolution result.