# Dance Dance Generation: Motion Transfer for Internet Videos

arXiv: https://arxiv.org/abs/1904.00129 · HTML: https://mlsanity.com/paper/1904.00129/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2019.

## Built on by (most cited first, 28 of 28)

- [DisCo: Disentangled Control for Realistic Human Dance Generation](https://mlsanity.com/paper/2307.00040/) (2023)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [Neural Re-Rendering of Humans from a Single Image](https://mlsanity.com/paper/2101.04104/) (2021)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [SignDiff: Diffusion Model for American Sign Language Production](https://mlsanity.com/paper/2308.16082/) (2023)
- [Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling](https://mlsanity.com/paper/2103.14338/) (2021)
- [Controllable Group Choreography using Contrastive Diffusion](https://mlsanity.com/paper/2310.18986/) (2023)
- [Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis](https://mlsanity.com/paper/2109.02081/) (2021)
- [Strumming to the Beat: Audio-Conditioned Contrastive Video Textures](https://mlsanity.com/paper/2104.02687/) (2021)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [Smile Like You Mean It: Driving Animatronic Robotic Face with Learned Models](https://mlsanity.com/paper/2105.12724/) (2021)
- [Flow Guided Transformable Bottleneck Networks for Motion Retargeting](https://mlsanity.com/paper/2106.07771/) (2021)
- [Fast-Vid2Vid: Spatial-Temporal Compression for Video-to-Video Synthesis](https://mlsanity.com/paper/2207.05049/) (2022)
- [VideoFACT: Detecting Video Forgeries Using Attention, Scene Context, and Forensic Traces](https://mlsanity.com/paper/2211.15775/) (2022)
- [WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction](https://mlsanity.com/paper/2502.01045/) (2025)
- [FDA-GAN: Flow-based Dual Attention GAN for Human Pose Transfer](https://mlsanity.com/paper/2112.00281/) (2021)
- [A Spatio-Temporal Representation Learning as an Alternative to Traditional Glosses in Sign Language Translation and Production](https://mlsanity.com/paper/2407.02854/) (2024)
- [Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis](https://mlsanity.com/paper/2507.06689/) (2025)
- [Video Reenactment as Inductive Bias for Content-Motion Disentanglement](https://mlsanity.com/paper/2102.00324/) (2021)
- [Bidirectional Temporal Diffusion Model for Temporally Consistent Human Animation](https://mlsanity.com/paper/2307.00574/) (2023)
- [SignLLM: Sign Language Production Large Language Models](https://mlsanity.com/paper/2405.10718/) (2024)
- [Discriminative Semantic Transitive Consistency for Cross-Modal Learning](https://mlsanity.com/paper/2103.14103/) (2021)
- [Do as we do: Multiple Person Video-To-Video Transfer](https://mlsanity.com/paper/2104.04721/) (2021)
- [DF-Captcha: A Deepfake Captcha for Preventing Fake Calls](https://mlsanity.com/paper/2208.08524/) (2022)
- [MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware Meta-learning](https://mlsanity.com/paper/2201.04851/) (2022)
- [Garment Animation NeRF with Color Editing](https://mlsanity.com/paper/2407.19774/) (2024)
- [Geometry-Aware Losses for Structure-Preserving Text-to-Sign Language Generation](https://mlsanity.com/paper/2509.23011/) (2025)
- [FlowDance: Music-Driven Dance Video Generation with Parallel Pose and RGB Streams](https://mlsanity.com/paper/2608.15818/) (2026)

## Abstract (arXiv)

This work presents computational methods for transferring body movements from one person to another with videos collected in the wild. Specifically, we train a personalized model on a single video from the Internet which can generate videos of this target person driven by the motions of other people. Our model is built on two generative networks: a human (foreground) synthesis net which generates photo-realistic imagery of the target person in a novel pose, and a fusion net which combines the generated foreground with the scene (background), adding shadows or reflections as needed to enhance realism. We validate the the efficacy of our proposed models over baselines with qualitative and quantitative evaluations as well as a subjective test.