Component-Based Transformation for Person Image Generation

Wen-Jiin Tsai, Po-Hsiang Chen

Abstract: Person image generation has attracted more and more attention because it has many applications. This paper focuses on pose transfer and virtual try-on. We propose a flow-based method called attribute-decomposited spatial transformation network. It warps different body parts separately using segmentation and then fuses different body parts and generates images. The flow-based technique enables the model to generate high-quality person images for pose transfer and the attribute-decomposited technique enables the model to support virtual try-on. The proposed method was evaluated on Deepfashion dataset. Quantitative and qualitative experimental results show the superiority of the method.