FuseAnyPart: Diffusion-Driven Facial Parts Swapping via Multiple Reference Images

Zheng Yu, Yaohua Wang, Siying Cui, Aixi Zhang, Wei-Long Zheng, Senzhang Wang

Keywords: diffusion model, personalization, image generation

TL;DR: A novel diffusion-driven facial parts swapping methods with multiple reference images.

Abstract:

Facial parts swapping aims to selectively transfer regions of interest from the source image onto the target image while maintaining the rest of the target image unchanged.
Most studies on face swapping designed specifically for full-face swapping, are either unable or significantly limited when it comes to swapping individual facial parts, which hinders fine-grained and customized character designs.
However, designing such an approach specifically for facial parts swapping is challenged by a reasonable multiple reference feature fusion, which needs to be both efficient and effective.
To overcome this challenge, FuseAnyPart is proposed to facilitate the seamless "fuse-any-part" customization of the face.
In FuseAnyPart, facial parts from different people are assembled into a complete face in latent space within the Mask-based Fusion Module.
Subsequently, the consolidated feature is dispatched to the Addition-based Injection Module for
fusion within the UNet of the diffusion model to create novel characters.
Extensive experiments qualitatively and quantitatively validate the superiority and robustness of FuseAnyPart.
Source codes are available at https://github.com/Thomas-wyh/FuseAnyPart.

Primary Area: Diffusion based models

Submission Number: 6702