# fudan-generative-vision/champ

[ECCV 2024] Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance

Repository: https://github.com/fudan-generative-vision/champ
Canonical: https://ross.abutalabs.com/products/champ
Homepage: https://fudan-generative-vision.github.io/champ/
Language: Python
License: MIT
License Family: permissive
Topics: human-animation, video-generation, image-animatioln
Last push: 2024-07-10T07:53:06+00:00

## Health v2 (maintenance only)
Score: 25/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 64
- inputs: {"age_days": 899, "days_push": 784, "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 4261, forks 485 (observed 2026-08-28T04:08:40.612635+00:00)

## What it is
Champ is a research framework for controllable and consistent human image animation using 3D parametric guidance (SMPL-based depth, normal, and semantic maps) with diffusion models. It animates a single reference human image according to motion from a driving video, and includes training code and SMPL preprocessing scripts.

## Use cases
- animate a photo of a person using motion from a dance video
- generate consistent human motion videos from a single image
- preprocess videos into SMPL pose data for animation
- train a custom human animation model
- retarget human motion between characters

## When to choose
- you need pose-controllable human image-to-video animation with 3D guidance
- you want an open research codebase for human animation with training support
- you have GPU resources and want to animate character images with driving motion

## When to avoid
- you need general-purpose text-to-video generation without human pose control
- you lack a GPU or cannot run diffusion model inference locally
- you need a polished end-user application rather than a research codebase

## Facets
- artifact type: library
- maturity: maintenance
- function: video-processing, machine-learning, deep-learning, image-processing
- domain: computer-vision, deep-learning, artificial-intelligence
- platform: python
- tags: human-animation, video-generation, diffusion-models, smpl, pose-guided-animation, eccv-2024, video, gpu, linux

## Member repositories
- fudan-generative-vision/champ (main) score 25

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:40.612635+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 2026-08-29T18:22:05.267093+00:00, confidence not recorded.
  - readme: https://github.com/fudan-generative-vision/champ (fetched 2026-08-28T04:08:40.612635+00:00, sha 63e17f6f505f)
  - homepage: https://fudan-generative-vision.github.io/champ/ (fetched 2026-08-29T09:11:37.693945+00:00, sha 59c784fc7e4f)
- Data as of 2026-08-30T08:39:29.467469+00:00.