StdGEN: Semantic-Decomposed 3D Character Generation
| Property | Value |
|---|---|
| Author | hyz317 |
| Paper | arXiv:2411.05738 |
| Model Repository | Hugging Face |
What is StdGEN?
StdGEN represents a breakthrough in 3D character generation, offering a sophisticated pipeline that transforms single images into high-quality 3D character models through semantic decomposition. The system comprises three main components: canonicalization, multi-view generation, and mesh reconstruction.
Implementation Details
The model architecture is split into three specialized models:
- StdGEN-canonicalize-1024: Specializes in converting reference character images to A-pose
- StdGEN-multiview-1024: Generates multi-view images and normals from A-pose images
- StdGEN-mesh-slrm: Implements Semantic-aware Large Reconstruction Model for final 3D mesh generation
Core Capabilities
- Single-image to 3D character conversion
- Semantic decomposition for improved accuracy
- Multi-view generation with normal mapping
- High-quality mesh reconstruction
Frequently Asked Questions
Q: What makes this model unique?
StdGEN's uniqueness lies in its semantic decomposition approach and three-stage pipeline, which enables high-quality 3D character generation from just a single image input.
Q: What are the recommended use cases?
The model is particularly suited for character artists, game developers, and animation studios needing to create 3D character models from 2D references quickly and efficiently.
