# Drexubery/ViewCrafter

[TPAMI 2025]  ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis

Repository: https://github.com/Drexubery/ViewCrafter
Canonical: https://ross.abutalabs.com/products/viewcrafter
Homepage: https://drexubery.github.io/ViewCrafter/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-12-13T03:02:50+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 57, release rhythm 35, longevity 55
- inputs: {"age_days": 772, "days_push": 263, "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 1587, forks 56 (observed 2026-08-28T04:05:07.811548+00:00)

## What it is
ViewCrafter is a research codebase that uses video diffusion models to synthesize high-fidelity novel views of scenes from a single or sparse reference images, with precise camera pose control. It supports iterative view synthesis and can feed generated views into 3D Gaussian Splatting optimization for immersive rendering and text-to-3D applications.

## Use cases
- generate novel views of a scene from a single image
- synthesize camera trajectory videos with pose control
- create training views for 3D gaussian splatting from sparse images
- scene-level text-to-3D content generation
- zero-shot novel view synthesis research
- reconstruct 3D scenes without dense multi-view captures

## When to choose
- you need novel view synthesis from one or a few images without training a per-scene model
- you want precise camera pose control over generated views
- you need generated views to bootstrap 3D-GS optimization
- you are doing research on diffusion-based 3D generation

## When to avoid
- you need real-time interactive view synthesis out of the box
- you lack a GPU or cannot run large diffusion models
- you need metrically accurate 3D reconstruction from dense captures
- you want a production-ready application rather than research code

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing, video-processing, computer-vision, graphics, stable-diffusion
- domain: computer-vision, deep-learning, graphics, artificial-intelligence, image-processing
- platform: python
- tags: novel-view-synthesis, video-diffusion, 3d-reconstruction, gaussian-splatting, camera-pose-control, research-code, tpami, video, gpu, linux

## Member repositories
- Drexubery/ViewCrafter (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.811548+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 2026-08-30T03:55:21.484294+00:00, confidence not recorded.
  - readme: https://github.com/Drexubery/ViewCrafter (fetched 2026-08-28T04:05:07.811548+00:00, sha 2f483b1d6eb7)
  - homepage: https://drexubery.github.io/ViewCrafter/ (fetched 2026-08-29T11:25:53.551768+00:00, sha e9804165f77c)
- Data as of 2026-08-30T08:39:29.467469+00:00.