deepseek-ai/DreamCraft3D

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3021 stars · 357 forks observed · 2026-08-28

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

Official PyTorch implementation of DreamCraft3D, an ICLR 2024 hierarchical 3D content generation method that turns a single 2D image into a high-fidelity, coherent 3D object. It combines score distillation sampling for geometry sculpting with a bootstrapped, DreamBooth-personalized diffusion prior for photorealistic texture boosting.

Use cases

• generate a 3d model from a single image
• create textured 3d meshes from a reference photo
• image to 3d asset generation with diffusion models
• produce photorealistic 3d objects for games or rendering
• research score distillation for 3d generation
• turn concept art into a 3d mesh

When to choose

• you need to convert a single 2D image into a coherent, textured 3D mesh
• you want a research-grade implementation of score distillation and bootstrapped diffusion priors
• you have GPU resources and want state-of-the-art single-image 3D generation quality

When to avoid

• you need fast, real-time 3D generation - optimization is slow and GPU-intensive
• you want a polished end-user application rather than research code
• you lack a CUDA-capable GPU or the environment to run diffusion models

Facets

library · maturity stable

machine-learning deep-learning image-processing graphics simulation artificial-intelligence machine-learning graphics computer-vision python 3d-generation diffusion-models image-to-3d score-distillation text-to-3d aigc generative-models research-code gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="deepseek-ai/DreamCraft3D")