FusedVTON: Early Image Fusion for Efficient Virtual Try-On

Existing image-based virtual try-on methods typically use explicit garment warping or model garment–person correspondence through reference networks, cross-attention, or self-attention over concatenated tokens. Although effective, these designs introduce architectural complexity and computational overhead, especially in multi-garment try-on, where additional garments require additional warping operations or garment representations. We view garment transfer as coarse spatial placement followed by non-rigid generative refinement and propose $\text{FusedVTON}$ to implement this formulation. Reference garments are resized and placed within the clothing-agnostic person image before VAE encoding. The resulting composite is processed through a single, fixed-length image-token stream without a separate garment branch or appended garment tokens. Experiments on VITON-HD, DressCode, DressCode-MR, and Garments2Look show competitive quality. Separate profiling at $512\times 384$ without classifier-free guidance measures 53.9% and 51.7% fewer model-inference FLOPs than the corresponding SD1.5 and FLUX.1 CatVTON variants. At $1024\times 768$, counted model-inference computation remains constant across one to four garment inputs in the evaluated scaling setup.