# Single-Shot Freestyle Dance Reenactment

arXiv: https://arxiv.org/abs/2012.01158 · HTML: https://mlsanity.com/paper/2012.01158/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors](https://mlsanity.com/paper/2203.13131/) (2022)
- [Human MotionFormer: Transferring Human Motions with Vision Transformers](https://mlsanity.com/paper/2302.11306/) (2023)
- [When Creators Meet the Metaverse: A Survey on Computational Arts](https://mlsanity.com/paper/2111.13486/) (2021)
- [Learning Motion-Dependent Appearance for High-Fidelity Rendering of Dynamic Humans from a Single Camera](https://mlsanity.com/paper/2203.12780/) (2022)
- [Transfer Learning for Pose Estimation of Illustrated Characters](https://mlsanity.com/paper/2108.01819/) (2021)
- [Conditional Temporal Variational AutoEncoder for Action Video Prediction](https://mlsanity.com/paper/2108.05658/) (2021)
- [Collaborative Neural Rendering using Anime Character Sheets](https://mlsanity.com/paper/2207.05378/) (2022)
- [REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer](https://mlsanity.com/paper/2209.00475/) (2022)
- [JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting](https://mlsanity.com/paper/2106.09679/) (2021)
- [Solutions to Deepfakes: Can Camera Hardware, Cryptography, and Deep Learning Verify Real Images?](https://mlsanity.com/paper/2407.04169/) (2024)

## Abstract (arXiv)

The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer. In this work, we propose a novel method that can reanimate a single image by arbitrary video sequences, unseen during training. The method combines three networks: (i) a segmentation-mapping network, (ii) a realistic frame-rendering network, and (iii) a face refinement network. By separating this task into three stages, we are able to attain a novel sequence of realistic frames, capturing natural motion and appearance. Our method obtains significantly better visual quality than previous methods and is able to animate diverse body types and appearances, which are captured in challenging poses, as shown in the experiments and supplementary video.