# hustvl/4DGaussians

[CVPR 2024] 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering

Repository: https://github.com/hustvl/4DGaussians
Canonical: https://ross.abutalabs.com/products/4dgaussians
Homepage: https://guanjunwu.github.io/4dgs/
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: 3d, computer-vision, dynamic-scene, gaussian-splatting, graphics, neural-network, neural-rendering, novel-view-synthesis, radiance-field, cvpr2024
Last push: 2024-10-27T04:07:37+00:00

## Health v2 (maintenance only)
Score: 27/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 76
- inputs: {"age_days": 1065, "days_push": 675, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3895, forks 385 (observed 2026-08-28T04:08:28.458705+00:00)

## What it is
An official PyTorch implementation of 4D Gaussian Splatting (4D-GS) for real-time rendering of dynamic scenes, published at CVPR 2024. It combines 3D Gaussians with 4D neural voxel encoding to train high-resolution dynamic scenes in ~30 minutes and render at 80+ FPS on a single RTX 3090.

## Use cases
- render dynamic 3D scenes in real time
- novel view synthesis of moving scenes from video
- train a 4D gaussian splatting model on D-NeRF or HyperNeRF datasets
- reproduce CVPR 2024 4D-GS research results
- compare dynamic scene rendering methods against radiance fields

## When to choose
- you need real-time rendering of dynamic scenes with high quality
- you want a fast-training alternative to per-frame 3D Gaussian Splatting
- you are doing research on neural rendering or radiance fields

## When to avoid
- you only need static scene reconstruction (plain 3D Gaussian Splatting suffices)
- you lack a CUDA GPU or cannot build custom rasterization submodules
- you need a production-ready application rather than research code

## Facets
- artifact type: library
- maturity: active
- function: graphics, computer-vision, machine-learning, simulation
- domain: computer-vision, graphics, deep-learning
- platform: python, cli
- tags: gaussian-splatting, novel-view-synthesis, dynamic-scenes, neural-rendering, radiance-field, cvpr-2024, research-code, gpu, linux

## Member repositories
- hustvl/4DGaussians (main) score 27

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:28.458705+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 2026-08-29T18:25:35.486017+00:00, confidence not recorded.
  - readme: https://github.com/hustvl/4DGaussians (fetched 2026-08-28T04:08:28.458705+00:00, sha f23e1b60e51b)
  - homepage: https://guanjunwu.github.io/4dgs/ (fetched 2026-08-29T09:19:23.910329+00:00, sha 3dec5d9f1bae)
- Data as of 2026-08-30T08:39:29.467469+00:00.