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With the development of Generative Adversarial Network, image-based virtualtry-on methods have made great progress. However, limited work has explored thetask of video-based virtual try-on while it is important in real-worldapplications. Most existing video-based virtual try-on methods usually requireclothing templates and they can only generate blurred and low-resolutionresults. To address these challenges, we propose a Memory-based Video virtualTry-On Network (MV-TON), which seamlessly transfers desired clothes to a targetperson without using any clothing templates and generates high-resolutionrealistic videos. Specifically, MV-TON consists of two modules: 1) a try-onmodule that transfers the desired clothes from model images to frame images bypose alignment and region-wise replacing of pixels; 2) a memory refinementmodule that learns to embed the existing generated frames into the latent spaceas external memory for the following frame generation. Experimental resultsshow the effectiveness of our method in the video virtual try-on task and itssuperiority over other existing methods.

MV-TON: Memory-based Video Virtual Try-on network