Garment Transfer

Garment transfer, a subfield of image generation, aims to realistically replace the clothing on a person in an image with a different garment. Current research focuses on improving the accuracy and realism of transferred garments, particularly addressing challenges like handling loose clothing, diverse poses, and cluttered backgrounds, often employing generative models and techniques like pixel or vertex flow to achieve accurate warping and texture preservation. These advancements leverage both paired and unpaired image datasets, with some methods utilizing self-supervised learning from video data to reduce the need for laborious manual annotation. The resulting improvements in garment transfer technology have significant implications for virtual try-on applications and the broader field of human-centric image generation.