Magic3D
NVIDIA's coarse-to-fine text-to-3D: low-res NeRF first, then a high-resolution textured mesh.
About
Magic3D improved on DreamFusion with a two-stage pipeline: a coarse NeRF optimized with a low-resolution diffusion prior and a sparse hash grid, then a textured 3D mesh refined with a high-resolution latent diffusion model and an efficient differentiable renderer. The paper reports meshes in about 40 minutes, roughly twice as fast as DreamFusion, with 8x higher resolution supervision, and 61.7% of raters preferred its outputs in the authors' user study. It also supports image conditioning and prompt-based editing of the coarse stage.
Published by NVIDIA Research in late 2022 and presented at CVPR 2023, Magic3D set the template for coarse-to-fine text-to-3D that later systems copied. Like DreamFusion, NVIDIA never released official code or weights; the method lives on through community reimplementations in frameworks like threestudio.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
61.7% rater preference over DreamFusion in the author-run user study (paper); 2x faster with 8x resolution supervision (author-reported)
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 7/10
Scored 7/10 (verified tier, metrics-v1). Evidence: CVPR 2023 peer-reviewed paper from NVIDIA Research with an author-run user study; the method is widely reimplemented. Strengths: influential coarse-to-fine design with strong reported gains over DreamFusion. Docked for: (1) no official code, weights, or API ever released; (2) all quality claims are author-run with no independent replication. License: research publication, no license offered.
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 8.0/10
- Maintenance · recency, releases, issue load
- 3.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · NVIDIA Research; arXiv Nov 2022, CVPR 2023; coarse-to-fine text-to-3D
- · ~40min per mesh, 2x faster than DreamFusion, 8x resolution supervision (author-reported)
- · 61.7% rater preference over DreamFusion in author-run user study
- · All quality claims author-run; community reimplementations only