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

• URL: http://arxiv.org/abs/2407.10625v1
• Date: Mon, 15 Jul 2024 11:21:03 GMT
• Title: WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models
• Authors: Zijian He, Peixin Chen, Guangrun Wang, Guanbin Li, Philip H. S. Torr, Liang Lin,
• Abstract summary: 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. 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.
• Score: 132.77237314239025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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