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MV-TON: Memory-based Video Virtual Try-on network
Open accessAbstract
With the development of Generative Adversarial Network, image-based virtual try-on methods have made great progress. However, limited work has explored the task of video-based virtual try-on while it is important in real-world applications. Most existing video-based virtual try-on methods usually require clothing templates and they can only generate blurred and low-resolution results. To address these challenges, we propose a Memory-based Video virtual Try-On Network (MV-TON), which seamlessly transfers desired clothes to a target person without using any clothing templates and generates high-resolution realistic videos. Specifically, MV-TON consists of two modules: 1) a try-on module that transfers the desired clothes from model images to frame images by pose alignment and region-wise replacing of pixels; 2) a memory refinement module that learns to embed the existing generated frames into the latent space as external memory for the following frame generation. Experimental results show the effectiveness of our method in the video virtual try-on task and its superiority over other existing methods.
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“MV-TON (Zhong et al., 2021) introduces a memory refinement module that embeds generated frames into a latent space as external memory, aiding subsequent frame generation with richer context.”
From The AI Hippocampus: How Far are We From Human Memory? (2026) · cites this paper as Zhong et al., 2021
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Cited by
The AI Hippocampus: How Far are We From Human Memory?
Jia, Z., Li, J., Kang, Y., Wang, Y., Wu, T., Wang, Q., Wang, X., Zhang, S., Shen, J., Li, Q., et al. (2026) · arXiv · doi:10.48550/arxiv.2601.09113
Metadata
- Authors
- Zhong, X., Wu, Z., Tan, T., Lin, G., & Wu, Q.
- Affiliations
- South China University of Technology (CN)
- Nanyang Technological University (SG)
- Guangdong University of Technology (CN)
- Venue
- Proceedings of the 29th ACM International Conference on Multimedia
- Publisher
- ACM
- Published
- 2021-10-17
- Pages
- 908-916
- OpenAlex
- W3195594845
- Cited by
- 29
- References
- 0
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 10 · 2023: 5 · 2022: 4 · 2021: 1
- FWCI
- 1.64
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- MOE Tier-1 research grants (RG22/19 (S), RG95/20, RG28/18 (S))
- Central Universities of China (D2192860)
- Key-Area Research and Development Program of Guangdong Province (2018B010108002)
- RIE2020 Industry Alignment Fund - Industry Collabo- ration Projects (NA)
- National Natural Science Foundation of China (61876208)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- external memory, pose alignment, high-resolution video generation, latent space embedding
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