RealVVT: Towards Photorealistic Video Virtual Try-on via Spatio-Temporal Consistency
This addresses the challenge of photorealistic virtual try-on for fashion e-commerce and virtual fitting, though it appears incremental as it builds on existing video foundation models.
The paper tackled the problem of maintaining consistent and realistic clothing appearance in video virtual try-on, and the result was that RealVVT outperformed state-of-the-art models in both single-image and video tasks, offering a viable solution for practical applications.
Virtual try-on has emerged as a pivotal task at the intersection of computer vision and fashion, aimed at digitally simulating how clothing items fit on the human body. Despite notable progress in single-image virtual try-on (VTO), current methodologies often struggle to preserve a consistent and authentic appearance of clothing across extended video sequences. This challenge arises from the complexities of capturing dynamic human pose and maintaining target clothing characteristics. We leverage pre-existing video foundation models to introduce RealVVT, a photoRealistic Video Virtual Try-on framework tailored to bolster stability and realism within dynamic video contexts. Our methodology encompasses a Clothing & Temporal Consistency strategy, an Agnostic-guided Attention Focus Loss mechanism to ensure spatial consistency, and a Pose-guided Long Video VTO technique adept at handling extended video sequences.Extensive experiments across various datasets confirms that our approach outperforms existing state-of-the-art models in both single-image and video VTO tasks, offering a viable solution for practical applications within the realms of fashion e-commerce and virtual fitting environments.