SeparateGen: Semantic Component-Based 3D Character Generation From Single Images
IEEE Transactions on Visualization and Computer Graphics
|January 12, 2026
Summary
SeparateGen creates high-quality 3D characters from single images by separating semantic components. This novel approach resolves mesh entanglement, enabling easier rigging and animation for 3D assets.
Area of Science:
- Computer Vision
- Computer Graphics
- Artificial Intelligence
Background:
- Generating detailed 3D characters from single images is difficult due to semantic component entanglement.
- Existing methods often result in poor mesh topology, limiting usability in animation and rigging.
Purpose of the Study:
- Introduce SeparateGen, a framework for high-quality 3D character generation from single images.
- Explicitly reconstruct characters into distinct semantic components (body, clothing, hair, shoes).
Main Methods:
- Utilize a multi-view diffusion model for consistent canonical A-pose image generation.
- Employ a novel component-aware reconstruction model (SC-LRM) for high-fidelity component decomposition and reconstruction.
- Introduce SC-Anime, a large-scale dataset of 7,580 anime-style 3D characters with component-level annotations.
Main Results:
- SeparateGen significantly outperforms state-of-the-art methods in reconstruction quality and multi-view consistency.
- The component-based approach effectively resolves mesh entanglement issues.
- Generated characters are suitable for seamless rigging and asset reuse.
Conclusions:
- SeparateGen advances the generation of application-ready 3D characters from single images.
- The SC-Anime dataset and code will be publicly released to facilitate further research.
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