DreamTechAI/Direct3D-S2

Health v2 · maintenance only

Adoption not part of the score

1274 stars · 107 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Direct3D-S2 is a research framework for high-resolution 3D shape generation from images, built on sparse volumetric representations and a novel Spatial Sparse Attention mechanism for Diffusion Transformers. It includes a unified sparse-volume VAE and pretrained models enabling gigascale (1024^3) 3D generation on as few as 8 GPUs.

Use cases

• generate a 3D model from a single image
• create high-resolution 3D meshes for games
• convert photos into 3D assets
• research sparse attention for 3D diffusion models
• train 3D generative models on limited GPUs
• reconstruct 3D shapes with SDF volumetric representations

When to choose

• you need image-to-3D generation with very high volumetric resolution
• you are researching efficient attention for sparse 3D data or DiT models
• you want a state-of-the-art open 3D generation model with released weights
• you have GPU resources and want to run or fine-tune 3D AIGC models

When to avoid

• you need a polished end-user 3D creation app rather than research code
• you have no GPU available, since inference requires significant VRAM
• you need text-to-3D or full texturing pipelines out of the box
• you need production support or a stable API contract

Facets

library · maturity active

machine-learning deep-learning image-processing graphics artificial-intelligence machine-learning graphics python 3d-generation image-to-3d sparse-attention diffusion-transformer sdf 3d-reconstruction research-model neurips-2025 game-development gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="DreamTechAI/Direct3D-S2")