title: CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On
    with Temporal Concatenation
summary: 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, most existing methods struggle to achieve high-quality
  results across image and video try-on tasks, especially in long video
  scenarios. In this work, we introduce CatV2TON, a simple and effective
  vision-based virtual try-on (V2TON) method that supports both image and video
  try-on tasks with a single diffusion transformer model. By temporally
  concatenating garment and person inputs and training on a mix of image and
  video datasets, CatV2TON achieves robust try-on performance across static and
  dynamic settings. For efficient long-video generation, we propose an
  overlapping clip-based inference strategy that uses sequential frame guidance
  and Adaptive Clip Normalization (AdaCN) to maintain temporal consistency with
  reduced resource demands. We also present ViViD-S, a refined video try-on
  dataset, achieved by filtering back-facing frames and applying 3D mask
  smoothing for enhanced temporal consistency. Comprehensive experiments
  demonstrate that CatV2TON outperforms existing methods in both image and video
  try-on tasks, offering a versatile and reliable solution for realistic virtual
  try-ons across diverse scenarios.
paper_authors._id: 6795f11746f22e87c8ab5895
paper_authors.name: Zheng Chong
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-29T08:55:29.276Z
paper_authors._id: 646446517572c66a8e652e94
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/646446517572c66a8e652e94/A4LIdECGW03ixc0HfIhfo.png
paper_authors.fullname: ZhengChong
paper_authors.type: user
paper_authors.user: zhengchong
paper_authors._id: 6795f11746f22e87c8ab5896
paper_authors.name: Wenqing Zhang
paper_authors._id: 6795f11746f22e87c8ab5897
paper_authors.name: Shiyue Zhang
paper_authors._id: 6795f11746f22e87c8ab5898
paper_authors.name: Jun Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-26T11:38:25.373Z
paper_authors._id: 6381847a471a4550ff298c63
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6381847a471a4550ff298c63/RTKepvX67R6pLiiUidpUO.png
paper_authors.fullname: Jun
paper_authors.type: user
paper_authors.user: zxbsmk
paper_authors._id: 6795f11746f22e87c8ab5899
paper_authors.name: Xiao Dong
paper_authors._id: 6795f11746f22e87c8ab589a
paper_authors.name: Haoxiang Li
paper_authors._id: 6795f11746f22e87c8ab589b
paper_authors.name: Yiling Wu
paper_authors._id: 6795f11746f22e87c8ab589c
paper_authors.name: Dongmei Jiang
paper_authors._id: 6795f11746f22e87c8ab589d
paper_authors.name: Xiaodan Liang
paper_title: CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On
    with Temporal Concatenation
paper_summary: 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, most existing methods struggle to achieve high-quality
  results across image and video try-on tasks, especially in long video
  scenarios. In this work, we introduce CatV2TON, a simple and effective
  vision-based virtual try-on (V2TON) method that supports both image and video
  try-on tasks with a single diffusion transformer model. By temporally
  concatenating garment and person inputs and training on a mix of image and
  video datasets, CatV2TON achieves robust try-on performance across static and
  dynamic settings. For efficient long-video generation, we propose an
  overlapping clip-based inference strategy that uses sequential frame guidance
  and Adaptive Clip Normalization (AdaCN) to maintain temporal consistency with
  reduced resource demands. We also present ViViD-S, a refined video try-on
  dataset, achieved by filtering back-facing frames and applying 3D mask
  smoothing for enhanced temporal consistency. Comprehensive experiments
  demonstrate that CatV2TON outperforms existing methods in both image and video
  try-on tasks, offering a versatile and reliable solution for realistic virtual
  try-ons across diverse scenarios.
paper_ai_summary: CatV2TON, a vision-based virtual try-on method using a diffusion transformer model, achieves high-quality results for both image and video try-on tasks, including efficient long-video generation through overlapping clip-based inference and adaptive clip normalization.
paper_ai_keywords: vision-based virtual try-on diffusion transformer model temporally concatenating adaptive clip normalization overlapping clip-based inference video try-on dataset 3D mask smoothing temporal consistency