DreamTechAI/Direct3D-S2
Direct3D-S2 is a scalable 3D generation framework that uses spatial sparse attention to generate high-resolution 3D shapes from images at gigascale.
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The framework addresses computational and memory challenges in volumetric 3D generation by leveraging sparse volume representations and sparse attention mechanisms. It generates 3D shapes represented as Signed Distance Functions (SDFs) from single or few images. The project includes inference code, pre-trained models, and a live demo on Hugging Face.
Frequently asked
- What is DreamTechAI/Direct3D-S2?
- Direct3D-S2 is a scalable 3D generation framework that uses spatial sparse attention to generate high-resolution 3D shapes from images at gigascale.
- Is Direct3D-S2 open source?
- Yes — DreamTechAI/Direct3D-S2 is open source, released under the MIT license.
- What language is Direct3D-S2 written in?
- DreamTechAI/Direct3D-S2 is primarily written in Python.
- How popular is Direct3D-S2?
- DreamTechAI/Direct3D-S2 has 1.3k stars on GitHub.
- Where can I find Direct3D-S2?
- DreamTechAI/Direct3D-S2 is on GitHub at https://github.com/DreamTechAI/Direct3D-S2.