Appearance-adapter: A Self-supervised Pose-guided Human Image Synthesis Approach

Abstract

Human image synthesis with pose guidance generates images of a specified human in a given pose, a task complicated by dis-occlusions and varying body articulations. While generative model-based approaches are effective, they often require paired training data, limiting generalizability. Recent selfsupervised methods, such as reconstruction from body parts and jigsaw puzzle-solving, face issues like pose leaking and inadequate appearance encoding. We propose a novel approach that learns to reconstruct images from body parts using a body symmetricity loss, leveraging human body symmetries. Our method preserves appearance information and mitigates pose leaking by aligning appearance features of corresponding body parts from symmetric left-right halves. Additionally, we leverage pretrained models, specifically stable-diffusion, to enhance performance and training efficiency. Extensive experiments and ablation studies on the deepfashion dataset demonstrate our method’s effectiveness.