Multi-scale attention guided pose transfer

Pose transfer refers to the probabilistic image generation of a person with a previously unseen novel pose from another image of that person having a different pose. Due to potential academic and commercial applications, pose transfer has been extensively studied in recent years. Among the various approaches to the problem, attention guided progressive generation is shown to produce state-of-the-art results in most cases. This paper presents an improved network architecture for pose transfer by introducing at-tention links at every resolution level of the encoder and decoder. By utilizing such dense multi-scale attention guided approach, we are able to achieve significant improvement over the existing methods both visually and analytically. We conclude our findings with extensive qualitative and quantitative com-parisons against several existing methods on the DeepFashion dataset. We also show the generality of the proposed network architecture by extending it to multiple application domains, such as semantic reconstruction, virtual try-on and style manipulation.1 (c) 2023 Elsevier Ltd. All rights reserved.