# nv-tlabs/GET3D

Repository: https://github.com/nv-tlabs/GET3D
Canonical: https://ross.abutalabs.com/products/get3d
Language: Python
License: NOASSERTION
License Family: other
Last push: 2024-09-27T15:54:44+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1455, "days_push": 705, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4435, forks 408 (observed 2026-08-28T04:08:49.253514+00:00)

## What it is
GET3D is NVIDIA's PyTorch implementation of a generative model that synthesizes high-quality 3D textured meshes (cars, chairs, animals, buildings) directly from 2D image collections. It combines differentiable surface modeling and rendering with 2D GANs to produce meshes usable in standard 3D rendering engines.

## Use cases
- generate 3d textured meshes from images
- create 3d assets for game engines automatically
- train a 3d generative model on a 2d image dataset
- generate procedural 3d content like cars and buildings
- research 3d gan and differentiable rendering methods

## When to choose
- you need to generate diverse, textured 3D meshes for virtual worlds or games
- you have 2D image collections and want to learn a 3D generative model
- you want a research-grade baseline for 3D GANs with pretrained Shapenet models

## When to avoid
- you lack high-end NVIDIA GPUs (V100/A100 recommended)
- you need production-supported software rather than a research codebase
- you need interactive 3D modeling rather than automated generation

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, graphics, simulation
- domain: deep-learning, computer-vision, graphics, artificial-intelligence
- platform: python
- tags: generative-model, 3d-mesh-generation, gan, textured-meshes, pytorch, research-code, differentiable-rendering, neurips-2022, linux, gpu

## Member repositories
- nv-tlabs/GET3D (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:49.253514+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:20:52.608182+00:00, confidence not recorded.
  - readme: https://github.com/nv-tlabs/GET3D (fetched 2026-08-28T04:08:49.253514+00:00, sha c37f8b3486b8)
- Data as of 2026-08-30T08:39:29.467469+00:00.