What is RealisHuman
RealisHuman is an innovative post-processing framework that refinesgenerated imageshuman body parts, such as hands and faces. The framework achieves this goal through a two-stage approach. First, it uses the original malformed parts as a reference to generate realistic human body parts, ensuring consistency with the original image. Second, by repainting the surrounding areas, it seamlessly integrates the corrected human body parts back into their original positions, ensuring smooth and realistic blending. RealisHuman improves the realism of human body parts in generated images. The framework also has strong generalization ability and can handle images in different styles, including cartoons and sketches.
Main Features of RealisHuman
- Generate realistic human body parts: RealisHuman uses the original malformed parts as a reference to generate realistic human body parts, such as hands and faces, ensuring that the generated parts are consistent with the original image in details.
- Seamlessly integrate corrected parts: After generating realistic parts, RealisHuman seamlessly integrates the corrected human body parts back into the original image by repainting the surrounding areas, ensuring smooth and realistic blending.
- Handle multiple image styles: It has strong generalization ability and can effectively handle images in different styles, including cartoons and sketches.
- Improve the realism of generated images: Through the above two-stage approach, RealisHuman improves the realism of human body parts in generated images.
Technical Principles of RealisHuman
- Stage 1: Generate realistic human body parts
- Reference the original malformed parts: RealisHuman uses the original malformed parts as a reference to ensure that the generated human body parts are consistent with the original image in details.
- Extract detailed information: By using tools such as Part Detail Encoder and DINOv2, it extracts detailed information from the original malformed parts, including skin color and texture.
- 3D pose estimation: It combines the 3D pose estimation results extracted from the original malformed parts to guide the generated human body part images, ensuring pose accuracy and realism.
- Stage 2: Seamlessly integrate corrected parts
- Localize and crop: First, place the corrected human body parts back into their original positions and mask the surrounding areas.
- Inpaint and blend: Treat this process as an inpainting problem and train a model to seamlessly blend the human body parts with the surrounding areas, ensuring smooth transitions and realistic blending.
- Repaint: During the integration process, repaint the area between the background and the corrected human body parts to avoid traces of collage.
RealisHuman Project Address
- Github Repository：https://github.com/Wangbenzhi/RealisHuman
- arXiv Technical Paper：https://arxiv.org/pdf/2409.03644
Application Scenarios of RealisHuman
- Game Development: In game character design, RealisHuman can be used to generate realistic hand and facial details for characters, enhancing the realism and immersion of the characters.
- Film and Television Production: In film and television special effects production, it can help repair or enhance characters' facial expressions and hand movements, making the special effects footage more natural and realistic.
- Image Restoration: For images with hand or facial deformities, RealisHuman can be used to repair these areas, making them look more natural.
- Artistic Creation: In artistic creation, artists can use this technology to generate human body parts with unique styles and details, enriching the expressiveness of artworks.
- Interactive Applications: In applications such as virtual fitting or virtual makeup, it can ensure that the hand and facial details of virtual characters remain consistent with the user's real features.