DiffBody: A One-Shot Approach for Realistic Pose and Shape Editing of Human Images Using Diffusion Models

Introduction

Pose and body shape editing in human images is a growing area of interest, but existing methods often fall short when it comes to making large edits without compromising realism or identity. Researchers from the University of Tsukuba have introduced DiffBody, a novel one-shot approach that addresses these challenges by leveraging 3D body models and diffusion-based refinement techniques.

Technical Overview

Key Innovations

• One-Shot Editing: DiffBody can perform significant pose and shape edits in a single step, preserving the subject's identity.
• 3D Model Fitting: The method fits a 3D body model to the input image, allowing for precise control over pose and shape changes.
• Diffusion-Based Refinement: An iterative refinement process with weak noise ensures that the edited image remains realistic and coherent.

How It Works

1. 3D Body Model Fitting

   • The first step involves fitting a 3D body model to the input image. This helps in accurately representing the subject's pose and shape.
   • The 3D model is then used to project the input image, creating an initial textured body model.

2. Pose and Shape Editing

   • Users can manipulate the 3D model to change the pose and shape of the human figure.
   • This step often introduces artifacts due to occlusions and inaccuracies in the 3D fitting process.

3. Diffusion-Based Refinement

   • To address these artifacts, DiffBody employs a diffusion-based refinement technique.
   • The initial rendered image undergoes an iterative refinement process with weak noise applied first to the whole body and then to the face.
   • This approach ensures that strong noise does not destroy the body structure and identity, while insufficient noise still helps in improving realism.

4. Realism Enhancement
   • To further enhance the realism of the edited image, DiffBody fine-tunes text embeddings using self-supervised learning.
   • This step helps in generating more natural-looking images by aligning the visual content with textual descriptions.

Implementation Details

• Dataset: The method was evaluated on various datasets to ensure its robustness and effectiveness.
• Benchmarks:
  • Quantitative Evaluation: DiffBody outperformed existing methods in terms of preserving identity and realism across different edits.
  • Qualitative Evaluation: Visual comparisons showed that DiffBody produced more natural and coherent results, even for large pose and shape changes.

Results

The researchers conducted both quantitative and qualitative evaluations to demonstrate the effectiveness of DiffBody. Key findings include:

• Identity Preservation: DiffBody maintained the subject's identity even after significant edits.
• Realism: The diffusion-based refinement process significantly improved the realism of the edited images.
• Versatility: The method performed well across various datasets, showing its robustness and generalizability.

Conclusion

DiffBody represents a significant advancement in the field of pose and shape editing for human images. By combining 3D body model fitting with diffusion-based refinement, it enables large edits while preserving identity and realism. This one-shot approach has the potential to revolutionize applications in areas such as virtual try-on, animation, and content creation.