IPVTON: Image-based 3D Virtual Try-on with Image Prompt Adapter
This addresses the need for accurate virtual try-on in e-commerce and fashion, representing an incremental improvement over existing methods.
The paper tackles the problem of realistically reconstructing a 3D human model wearing a garment from separate images of a person and clothing, achieving state-of-the-art performance in geometry and texture for image-based 3D virtual try-on.
Given a pair of images depicting a person and a garment separately, image-based 3D virtual try-on methods aim to reconstruct a 3D human model that realistically portrays the person wearing the desired garment. In this paper, we present IPVTON, a novel image-based 3D virtual try-on framework. IPVTON employs score distillation sampling with image prompts to optimize a hybrid 3D human representation, integrating target garment features into diffusion priors through an image prompt adapter. To avoid interference with non-target areas, we leverage mask-guided image prompt embeddings to focus the image features on the try-on regions. Moreover, we impose geometric constraints on the 3D model with a pseudo silhouette generated by ControlNet, ensuring that the clothed 3D human model retains the shape of the source identity while accurately wearing the target garments. Extensive qualitative and quantitative experiments demonstrate that IPVTON outperforms previous methods in image-based 3D virtual try-on tasks, excelling in both geometry and texture.