FW-VTON: Flattening-and-Warping for Person-to-Person Virtual Try-on
This addresses the problem of realistic virtual clothing try-on for e-commerce and fashion applications, though it appears incremental as it builds on existing virtual try-on methods by shifting from garment-to-person to person-to-person tasks.
The paper tackles the person-to-person virtual try-on problem, where a garment worn by one person is transferred to another person using only two input images, and demonstrates that their FW-VTON method achieves state-of-the-art performance with superior qualitative and quantitative results.
Traditional virtual try-on methods primarily focus on the garment-to-person try-on task, which requires flat garment representations. In contrast, this paper introduces a novel approach to the person-to-person try-on task. Unlike the garment-to-person try-on task, the person-to-person task only involves two input images: one depicting the target person and the other showing the garment worn by a different individual. The goal is to generate a realistic combination of the target person with the desired garment. To this end, we propose Flattening-and-Warping Virtual Try-On (\textbf{FW-VTON}), a method that operates in three stages: (1) extracting the flattened garment image from the source image; (2) warping the garment to align with the target pose; and (3) integrating the warped garment seamlessly onto the target person. To overcome the challenges posed by the lack of high-quality datasets for this task, we introduce a new dataset specifically designed for person-to-person try-on scenarios. Experimental evaluations demonstrate that FW-VTON achieves state-of-the-art performance, with superior results in both qualitative and quantitative assessments, and also excels in garment extraction subtasks.