CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On
  with Temporal Concatenation
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.
6795f11746f22e87c8ab5895
Zheng Chong
claimed_verified
2025-01-29T08:55:29.276Z
646446517572c66a8e652e94
https://cdn-avatars.huggingface.co/v1/production/uploads/646446517572c66a8e652e94/A4LIdECGW03ixc0HfIhfo.png
ZhengChong
user
zhengchong
6795f11746f22e87c8ab5896
Wenqing Zhang
6795f11746f22e87c8ab5897
Shiyue Zhang
6795f11746f22e87c8ab5898
Jun Zheng
claimed_verified
2025-01-26T11:38:25.373Z
6381847a471a4550ff298c63
https://cdn-avatars.huggingface.co/v1/production/uploads/6381847a471a4550ff298c63/RTKepvX67R6pLiiUidpUO.png
Jun
user
zxbsmk
6795f11746f22e87c8ab5899
Xiao Dong
6795f11746f22e87c8ab589a
Haoxiang Li
6795f11746f22e87c8ab589b
Yiling Wu
6795f11746f22e87c8ab589c
Dongmei Jiang
6795f11746f22e87c8ab589d
Xiaodan Liang
CatV2TON: Taming Diffusion Transformers for Vision-Based Virtual Try-On
  with Temporal Concatenation
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.
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.
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