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WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models

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Computer Vision – ECCV 2024 (ECCV 2024)

Abstract

Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person’s pose and body shape in source videos. Traditional image-based methods, relying on warping and blending, struggle with complex human movements and occlusions, limiting their effectiveness in video try-on applications. Moreover, video-based models require extensive, high-quality data and substantial computational resources. To tackle these issues, we reconceptualize video try-on as a process of generating videos conditioned on garment descriptions and human motion. Our solution, WildVidFit, employs image-based controlled diffusion models for a streamlined, one-stage approach. This model, conditioned on specific garments and individuals, is trained on still images rather than videos. It leverages diffusion guidance from pre-trained models including a video masked autoencoder for segment smoothness improvement and a self-supervised model for feature alignment of adjacent frame in the latent space. This integration markedly boosts the model’s ability to maintain temporal coherence, enabling more effective video try-on within an image-based framework. Our experiments on the VITON-HD and DressCode datasets, along with tests on the VVT and TikTok datasets, demonstrate WildVidFit’s capability to generate fluid and coherent videos. The project page website is at wildvidfit-project.github.io.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (NO. 62322608, NO. 62325605), in part by the Fundamental Research Funds for the Central Universities under Grant 22lgqb25, in part by the CAAI-MindSpore Open Fund, developed on OpenI Community, and in part by the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2023A01).

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Correspondence to Guanbin Li .

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He, Z., Chen, P., Wang, G., Li, G., Torr, P.H.S., Lin, L. (2025). WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15075. Springer, Cham. https://doi.org/10.1007/978-3-031-72643-9_8

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  • DOI: https://doi.org/10.1007/978-3-031-72643-9_8

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