5 papers
FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable Diffusion Models
Zheng Chong, Yanwei Lei, Shiyue Zhang +7
Despite its great potential, virtual try-on technology is hindered from real-world application by two major challenges: the inability of current methods to support multi-reference…
CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
Zheng Chong, Xiao Dong, Haoxiang Li +6
Virtual try-on methods based on diffusion models achieve realistic effects but often require additional encoding modules, a large number of training parameters, and complex preproc…
ComposeAnyone: Controllable Layout-to-Human Generation with Decoupled Multimodal Conditions
Shiyue Zhang, Zheng Chong, Xi Lu +6
Building on the success of diffusion models, significant advancements have been made in multimodal image generation tasks. Among these, human image generation has emerged as a prom…
CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On with Temporal Concatenation
Zheng Chong, Wenqing Zhang, Shiyue Zhang +6
Virtual try-on (VTON) technology has gained attention due to its potential to transform online retail by enabling realistic clothing visualization of images and videos. However, mo…
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
Jiehui Huang, Xiao Dong, Wenhui Song +9
Diffusion-based technologies have made significant strides, particularly in personalized and customized facialgeneration. However, existing methods face challenges in achieving hig…