# Copy Motion From One to Another: Fake Motion Video Generation

arXiv: https://arxiv.org/abs/2205.01373 · HTML: https://mlsanity.com/paper/2205.01373/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 23 of 23)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Video-to-Video Synthesis](https://mlsanity.com/paper/1808.06601/) (2018)
- [Everybody Dance Now](https://mlsanity.com/paper/1808.07371/) (2018)
- [Motion Representations for Articulated Animation](https://mlsanity.com/paper/2104.11280/) (2021)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Few-shot Video-to-Video Synthesis](https://mlsanity.com/paper/1910.12713/) (2019)
- [Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis](https://mlsanity.com/paper/1909.12224/) (2019)
- [Synthesizing Images of Humans in Unseen Poses](https://mlsanity.com/paper/1804.07739/) (2018)
- [PISE: Person Image Synthesis and Editing with Decoupled GAN](https://mlsanity.com/paper/2103.04023/) (2021)
- [Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis](https://mlsanity.com/paper/1810.11610/) (2018)
- [Deep Dual Consecutive Network for Human Pose Estimation](https://mlsanity.com/paper/2103.07254/) (2021)
- [Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation](https://mlsanity.com/paper/2008.12606/) (2020)
- [TransMoMo: Invariance-Driven Unsupervised Video Motion Retargeting](https://mlsanity.com/paper/2003.14401/) (2020)
- [High-Fidelity Neural Human Motion Transfer from Monocular Video](https://mlsanity.com/paper/2012.10974/) (2020)
- [Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis](https://mlsanity.com/paper/2011.09055/) (2020)
- [Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation](https://mlsanity.com/paper/2203.15227/) (2022)
- [Aggregated Multi-GANs for Controlled 3D Human Motion Prediction](https://mlsanity.com/paper/2103.09755/) (2021)
- [Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling](https://mlsanity.com/paper/2103.14338/) (2021)
- [C2F-FWN: Coarse-to-Fine Flow Warping Network for Spatial-Temporal Consistent Motion Transfer](https://mlsanity.com/paper/2012.08976/) (2020)

## Built on by (most cited first, 13 of 13)

- [Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection](https://mlsanity.com/paper/2403.01786/) (2024)
- [Locate and Verify: A Two-Stream Network for Improved Deepfake Detection](https://mlsanity.com/paper/2309.11131/) (2023)
- [DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues](https://mlsanity.com/paper/2309.09526/) (2023)
- [Do As I Do: Pose Guided Human Motion Copy](https://mlsanity.com/paper/2406.16601/) (2024)
- [Joint-Motion Mutual Learning for Pose Estimation in Videos](https://mlsanity.com/paper/2408.02285/) (2024)
- [Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation](https://mlsanity.com/paper/2412.19871/) (2024)
- [SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled Videos](https://mlsanity.com/paper/2501.15073/) (2025)
- [GC-ConsFlow: Leveraging Optical Flow Residuals and Global Context for Robust Deepfake Detection](https://mlsanity.com/paper/2501.13435/) (2025)
- [A Pioneering Neural Network Method for Efficient and Robust Fluid Simulation](https://mlsanity.com/paper/2412.10748/) (2024)
- [Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization](https://mlsanity.com/paper/2309.06877/) (2023)
- [ETGuard: Malicious Encrypted Traffic Detection in Blockchain-based Power Grid Systems](https://mlsanity.com/paper/2408.10657/) (2024)
- [Multi-Grained Feature Pruning for Video-Based Human Pose Estimation](https://mlsanity.com/paper/2503.05365/) (2025)
- [Unveiling Deepfakes: A Frequency-Aware Triple Branch Network for Deepfake Detection](https://mlsanity.com/paper/2604.17477/) (2026)

## Abstract (arXiv)

One compelling application of artificial intelligence is to generate a video of a target person performing arbitrary desired motion (from a source person). While the state-of-the-art methods are able to synthesize a video demonstrating similar broad stroke motion details, they are generally lacking in texture details. A pertinent manifestation appears as distorted face, feet, and hands, and such flaws are very sensitively perceived by human observers. Furthermore, current methods typically employ GANs with a L2 loss to assess the authenticity of the generated videos, inherently requiring a large amount of training samples to learn the texture details for adequate video generation. In this work, we tackle these challenges from three aspects: 1) We disentangle each video frame into foreground (the person) and background, focusing on generating the foreground to reduce the underlying dimension of the network output. 2) We propose a theoretically motivated Gromov-Wasserstein loss that facilitates learning the mapping from a pose to a foreground image. 3) To enhance texture details, we encode facial features with geometric guidance and employ local GANs to refine the face, feet, and hands. Extensive experiments show that our method is able to generate realistic target person videos, faithfully copying complex motions from a source person.