# Wangt-CN/DisCo

[CVPR2024] DisCo: Referring Human Dance Generation in Real World

Repository: https://github.com/Wangt-CN/DisCo
Canonical: https://ross.abutalabs.com/products/wangt-cn-disco
Homepage: https://disco-dance.github.io/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: aigc, controlnet, human-generation
Last push: 2024-07-22T14:04:42+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 84
- inputs: {"age_days": 1187, "days_push": 772, "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 1072, forks 101 (observed 2026-08-28T04:03:28.667635+00:00)

## What it is
DisCo is a CVPR 2024 research codebase for referring human dance generation, producing realistic dance images and videos from a reference human subject, background, and target pose using disentangled diffusion-based control. It includes model training/inference code, a temporal module for video synthesis, and demo notebooks.

## Use cases
- generate dance videos from a reference person image and pose sequence
- transfer a human pose onto an unseen subject while preserving appearance
- compose a person, background, and pose from different source images
- animate a character following skeleton keypoints
- research on controllable human image and video synthesis

## When to choose
- you need pose-conditioned human image/video generation with faithful foreground and background preservation
- you want a research baseline or demo for dance synthesis
- you need compositional control over subject, background, and pose from separate sources

## When to avoid
- you need a production-ready, supported product rather than research code
- you lack GPU resources for diffusion model inference or training
- your task is generic text-to-image generation without pose control

## Facets
- artifact type: library
- maturity: maintenance
- function: image-processing, video-processing, machine-learning, deep-learning
- domain: computer-vision, image-processing, artificial-intelligence, deep-learning
- platform: python
- tags: diffusion-models, pose-transfer, dance-generation, controlnet, aigc, cvpr2024, human-synthesis, video, linux, gpu

## Member repositories
- Wangt-CN/DisCo (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:28.667635+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 2026-08-30T06:53:40.235135+00:00, confidence not recorded.
  - readme: https://github.com/Wangt-CN/DisCo (fetched 2026-08-28T04:03:28.667635+00:00, sha 67a25b9581b8)
  - homepage: https://disco-dance.github.io/ (fetched 2026-08-29T12:55:50.697396+00:00, sha 2dba2bb5f220)
- Data as of 2026-08-30T08:39:29.467469+00:00.