2.5D pose guided human image generation

Abstract

In this paper, we propose a 2.5D pose guided human image generation method that integrates depth information with 2D poses. Given a target 2.5D pose and an image of a person, our method generates a new image of that person with the target pose. To incorporate depth information into the pose structure, we design a three-layer pose space that allows accurate pose transfer compared with regular 2D pose structure. Specifically, our pose space enables the generative models to address the occlusion problems commonly happened in human image generation and also helps recognize spatial front-back relations of limbs. Extensive quantitative and qualitative results on the DeepFashion and Human 3.6M datasets demonstrate the effectiveness of our method.