# NVlabs/eg3d

Repository: https://github.com/NVlabs/eg3d
Canonical: https://ross.abutalabs.com/products/eg3d
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-06-10T19:59:28+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": 1722, "days_push": 1180, "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 3338, forks 362 (observed 2026-08-28T04:07:56.801770+00:00)

## What it is
Official PyTorch implementation of EG3D, an efficient geometry-aware 3D generative adversarial network from NVIDIA Research (CVPR 2022). It synthesizes high-resolution multi-view-consistent images and 3D geometry from single-view 2D photo collections using a hybrid explicit-implicit network with neural rendering.

## Use cases
- generate multi-view-consistent images of faces or cats
- generate 3D shapes from 2D photographs
- render videos from a pre-trained 3D GAN model
- research 3D-aware image synthesis
- experiment with geometry-aware GAN architectures
- produce novel views of generated objects

## When to choose
- you need state-of-the-art 3D-aware GAN synthesis with geometry output
- you have high-end NVIDIA GPUs and want a research-grade reference implementation
- you want to build on StyleGAN2-style generators for 3D content

## When to avoid
- you lack CUDA-capable NVIDIA GPUs
- you need a production-ready 3D asset pipeline rather than research code
- you need a permissively licensed library for commercial use without checking NVIDIA licensing terms

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, machine-learning, image-processing, graphics
- domain: deep-learning, computer-vision, graphics, artificial-intelligence
- platform: python
- tags: 3d-gan, neural-rendering, triplegan, stylegan2, 3d-aware-synthesis, cvpr-2022, pytorch, research-code, linux, gpu

## Member repositories
- NVlabs/eg3d (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:56.801770+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 2026-08-29T18:41:05.823218+00:00, confidence not recorded.
  - readme: https://github.com/NVlabs/eg3d (fetched 2026-08-28T04:07:56.801770+00:00, sha 2f29f1cd40a6)
- Data as of 2026-08-30T08:39:29.467469+00:00.