The pipeline integrates several technical innovations to achieve its superior performance. It employs a differentiable multi-layer semantic surface extraction scheme to obtain meshes from hybrid implicit fields reconstructed by S-LRM. Additionally, a specialized efficient multi-view diffusion model and an iterative multi-layer surface refinement module work together to enhance the quality and decomposability of the generated 3D characters. This combination ensures that the final models are not only visually detailed but also semantically segmented, facilitating easy editing and customization for diverse applications.
Extensive experiments demonstrate that StdGEN surpasses existing state-of-the-art methods in terms of geometry, texture fidelity, and semantic decomposability. The model offers ready-to-use 3D characters that can be flexibly customized, making it highly applicable for content creators, game developers, and filmmakers who require fast and high-quality 3D character assets. The research team has open-sourced the inference code, pretrained checkpoints, and datasets, and provides an online demo to facilitate adoption and experimentation by the broader community.
Key features include:
- Generates semantically decomposed 3D characters from single images within minutes
- Transformer-based Semantic-aware Large Reconstruction Model (S-LRM) for joint geometry, color, and semantic reconstruction
- Differentiable multi-layer semantic surface extraction for accurate mesh generation
- Efficient multi-view diffusion and iterative surface refinement modules for high-quality outputs
- Produces editable 3D models with separate components such as body, clothes, and hair
- Open-source code, pretrained models, datasets, and online demo available
- Superior performance in geometry, texture, and decomposability compared to existing methods