CF-VTON: Multi-Pose Virtual Try-on with Cross-Domain Fusion

The multi-pose virtual try-on technology aims to seamlessly fit an in-shop garment onto a reference person in various poses. This technology has attracted considerable attention from researchers due to its potential commercial and practical applications. Previous works in this field have encountered issues such as unnatural garment alignment and difficulty in preserving the person’s identity, arising from the weak mapping relationship between different feature crosses. To address these challenges, this paper proposes a novel multi-pose virtual try-on network named CF-VTON. Our approach involves predicting the "after-try-on" semantic map to guide garment alignment and try-on synthesis, warping the garment using an improved garment alignment network (GANet) to optimize unnatural alignment, synthesizing a coarse result with our proposed try-on synthesis network (TSN), and refining the output to reconstruct the virtual try-on result with rich facial identity and garment details. Qualitative and quantitative experiments demonstrate the superiority of our approach, outperforming state-of-the-art methods in an efficient manner.