# fudan-zvg/4d-gaussian-splatting

[ICLR 2024] Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting

Repository: https://github.com/fudan-zvg/4d-gaussian-splatting
Canonical: https://ross.abutalabs.com/products/4d-gaussian-splatting
Language: Python
License: MIT
License Family: permissive
Last push: 2026-01-31T05:17:21+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 65, release rhythm 35, longevity 76
- inputs: {"age_days": 1069, "days_push": 214, "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 1025, forks 82 (observed 2026-08-28T04:03:16.636819+00:00)

## What it is
Official PyTorch/CUDA implementation of 4D Gaussian Splatting (ICLR 2024), which represents and renders dynamic scenes in real time using 4D Gaussian primitives with a dedicated rendering pipeline. Built on top of 3D Gaussian Splatting, it supports training on datasets like DyNeRF and D-NeRF for photorealistic dynamic novel view synthesis.

## Use cases
- render dynamic scenes in real time from multi-view video
- train a 4D gaussian splatting model on the DyNeRF dataset
- do dynamic novel view synthesis from monocular video
- reconstruct dynamic 3D scenes with gaussian primitives
- reproduce the ICLR 2024 4DGS paper results
- create bullet-time or free-viewpoint video effects

## When to choose
- you need real-time photorealistic rendering of dynamic scenes
- you want the official, citable implementation of the 4D Gaussian Splatting paper
- you are doing research on dynamic scene representation or novel view synthesis
- you already have a CUDA-capable GPU and are comfortable with the 3D Gaussian Splatting toolchain

## When to avoid
- you only need static scene reconstruction (plain 3D Gaussian Splatting is simpler)
- you have no CUDA GPU, since training and rendering require one
- you need a production-ready end-user application rather than research code
- you need cross-platform or CPU-only rendering

## Facets
- artifact type: library
- maturity: active
- function: graphics, machine-learning, image-processing, simulation
- domain: computer-vision, graphics, deep-learning, machine-learning
- platform: python, cpp
- tags: gaussian-splatting, novel-view-synthesis, neural-rendering, 4d-reconstruction, research-code, iclr-2024, linux, gpu, cuda

## Member repositories
- fudan-zvg/4d-gaussian-splatting (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:16.636819+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 2026-08-30T07:08:04.083195+00:00, confidence not recorded.
  - readme: https://github.com/fudan-zvg/4d-gaussian-splatting (fetched 2026-08-28T04:03:16.636819+00:00, sha 6a550f612315)
- Data as of 2026-08-30T08:39:29.467469+00:00.