Low-Barrier Dataset Collection With Real Human Body for Interactive Per-Garment Virtual Try-On

Existing image-based virtual try-on methods are limited to frontal views and lack real-time performance. While per-garment virtual try-on methods have tackled these issues by adopting per-garment training, they still encounter practical limitations: (1) the robotic mannequin used for per-garment datasets collection is prohibitively expensive; (2) the synthesized garments often misalign with the human body. To address these challenges, we propose a low-barrier approach to collect per-garment datasets using real human bodies, eliminating the need for an expensive robotic mannequin and reducing data collection time from 2 hours to 2 minutes. Additionally, we introduce a hybrid person representation that ensures precise human-garment alignment. We conducted qualitative and quantitative comparisons with state-of-the-art image-based virtual try-on methods to demonstrate the superiority of our method regarding image quality and temporal consistency. Furthermore, most participants in our user study found the system effective in supporting garment purchasing decisions.