# ingra14m/Deformable-3D-Gaussians

[CVPR 2024] Official implementation of "Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction"

Repository: https://github.com/ingra14m/Deformable-3D-Gaussians
Canonical: https://ross.abutalabs.com/products/deformable-3d-gaussians
Homepage: https://ingra14m.github.io/Deformable-Gaussians/
Language: Python
License: MIT
License Family: permissive
Last push: 2024-06-25T03:06:44+00:00

## Health v2 (maintenance only)
Score: 18/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 76
- inputs: {"age_days": 1077, "days_push": 799, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1255, forks 84 (observed 2026-08-28T04:04:08.929594+00:00)

## What it is
Official PyTorch implementation of the CVPR 2024 paper 'Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction'. It reconstructs dynamic scenes from monocular video using deformable 3D Gaussian Splatting with a deformation field, enabling real-time rendering and novel-view/time synthesis.

## Use cases
- reconstruct dynamic scenes from monocular video
- novel view synthesis of dynamic scenes
- real-time rendering of 4D scenes
- compare against D-NeRF and HyperNeRF baselines
- research deformable 3D Gaussian splatting
- render time-interpolated frames of a moving scene

## When to choose
- you need a high-fidelity, real-time alternative to NeRF-based dynamic scene methods
- you want the official reference implementation of the Deformable-3D-GS paper
- you work with D-NeRF, NeRF-DS, or HyperNeRF datasets
- you need explicit 3D representations for dynamic scenes

## When to avoid
- you need a production-ready application with a polished UI
- you lack a CUDA-capable GPU
- you need multi-view or static-scene-only reconstruction with minimal setup
- you need long-term maintenance or commercial support

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, graphics, image-processing, data-visualization
- domain: computer-vision, graphics, deep-learning, machine-learning
- platform: python
- tags: 3d-gaussian-splatting, dynamic-scene-reconstruction, novel-view-synthesis, monocular-video, cvpr-2024, neural-rendering, research-code, linux, gpu

## Member repositories
- ingra14m/Deformable-3D-Gaussians (main) score 18

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:08.929594+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 2026-08-30T05:07:19.947160+00:00, confidence not recorded.
  - readme: https://github.com/ingra14m/Deformable-3D-Gaussians (fetched 2026-08-28T04:04:08.929594+00:00, sha da1e1c43eb22)
  - homepage: https://ingra14m.github.io/Deformable-Gaussians/ (fetched 2026-08-29T12:17:56.013025+00:00, sha 3dafb5537e5b)
- Data as of 2026-08-30T08:39:29.467469+00:00.