# Tencent/DepthCrafter

[CVPR 2025 Highlight] DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos

Repository: https://github.com/Tencent/DepthCrafter
Canonical: https://ross.abutalabs.com/products/depthcrafter
Homepage: https://depthcrafter.github.io
Language: Python
License: NOASSERTION
License Family: other
Last push: 2025-11-30T17:38:09+00:00

## Health v2 (maintenance only)
Score: 38/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 54, release rhythm 8, longevity 52
- inputs: {"age_days": 728, "days_push": 276, "days_rel": 645, "gap_med": null, "n_releases_24m": 1}
- flags: prerelease_only, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1574, forks 87 (observed 2026-08-28T04:05:05.726900+00:00)

## What it is
DepthCrafter is a diffusion-based video depth estimation model from Tencent AI Lab that generates temporally consistent long depth sequences for open-world videos without requiring camera poses or optical flow. It ships as a Python library with inference scripts, a Hugging Face demo, and integrations into ComfyUI and Nuke.

## Use cases
- estimate depth maps from monocular video
- generate temporally consistent depth sequences for visual effects
- get depth conditioning for video generation
- convert video depth to point clouds or 3D effects
- process long videos with segment-wise depth estimation
- export depth data as EXR for compositing pipelines

## When to choose
- you need consistent video depth without camera pose or optical flow input
- you work with open-world videos of varying content and camera motion
- you want depth maps for VFX, compositing, or conditional video generation
- you need zero-shot depth estimation up to ~110 frames per pass

## When to avoid
- you only need single-image depth estimation
- you need real-time depth on CPU or edge devices
- you require metric (absolute-scale) depth rather than relative depth
- you need a lightweight model without GPU or diffusion inference costs

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, video-processing, image-processing
- domain: computer-vision, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: depth-estimation, video-depth, diffusion-model, monocular-depth, cvpr-2025, research, video, gpu, linux

## Member repositories
- Tencent/DepthCrafter (main) score 38

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.726900+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 2026-08-30T03:57:38.158765+00:00, confidence not recorded.
  - readme: https://github.com/Tencent/DepthCrafter (fetched 2026-08-28T04:05:05.726900+00:00, sha 1814abcb8c06)
  - homepage: https://depthcrafter.github.io (fetched 2026-08-29T11:27:55.558017+00:00, sha c84a715745ed)
- Data as of 2026-08-30T08:39:29.467469+00:00.