Summary of Catvton: Concatenation Is All You Need For Virtual Try-on with Diffusion Models, by Zheng Chong et al.
CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
by Zheng Chong, Xiao Dong, Haoxiang Li, Shiyue Zhang, Wenqing Zhang, Xujie Zhang, Hanqing Zhao, Dongmei Jiang, Xiaodan Liang
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: Virtual try-on methods based on diffusion models have achieved realistic effects but often require additional encoding modules, a large number of training parameters, and complex preprocessing. This paper re-evaluates the necessity of these modules and analyzes how to improve training efficiency and reduce redundant steps in the inference process. The proposed CatVTON model is a simple and efficient virtual try-on diffusion model that concatenates garments along spatial dimensions as inputs of the diffusion model. Key features include a lightweight network with only 899.06M parameters, parameter-efficient training using self-attention modules (49.57M), and simplified inference requiring minimal preprocessing. The paper demonstrates superior qualitative and quantitative results compared to baseline methods and strong generalization performance in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Imagine trying on clothes virtually without having to physically go to a store. This can be done using special computer models, but they often need lots of information and processing power. Researchers have developed a new model called CatVTON that makes virtual try-on simpler and more efficient. It uses less data and computing power than other methods while still producing high-quality results. The model is designed to work well in real-world situations, such as trying on clothes online. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Generalization » Inference » Parameter efficient » Self attention
