# Can Pose Transfer Models Generate Realistic Human Motion?

arXiv: https://arxiv.org/abs/2501.15648 · HTML: https://mlsanity.com/paper/2501.15648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 30 of 30)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation](https://mlsanity.com/paper/2311.17117/) (2023)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model](https://mlsanity.com/paper/2311.16498/) (2023)
- [Motion Representations for Articulated Animation](https://mlsanity.com/paper/2104.11280/) (2021)
- [Pose Guided Person Image Generation](https://mlsanity.com/paper/1705.09368/) (2017)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion](https://mlsanity.com/paper/2304.06025/) (2023)
- [Deformable GANs for Pose-based Human Image Generation](https://mlsanity.com/paper/1801.00055/) (2017)
- [Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis](https://mlsanity.com/paper/1909.12224/) (2019)
- [Progressive Pose Attention Transfer for Person Image Generation](https://mlsanity.com/paper/1904.03349/) (2019)
- [DwNet: Dense warp-based network for pose-guided human video generation](https://mlsanity.com/paper/1910.09139/) (2019)
- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [Expressive Whole-Body 3D Gaussian Avatar](https://mlsanity.com/paper/2407.21686/) (2024)
- [X-Avatar: Expressive Human Avatars](https://mlsanity.com/paper/2303.04805/) (2023)
- [High-Fidelity Neural Human Motion Transfer from Monocular Video](https://mlsanity.com/paper/2012.10974/) (2020)
- [Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfer](https://mlsanity.com/paper/2307.07754/) (2023)
- [Image-Based Virtual Try-On: A Survey](https://mlsanity.com/paper/2311.04811/) (2023)
- [Human Motion Transfer from Poses in the Wild](https://mlsanity.com/paper/2004.03142/) (2020)
- [Edit Temporal-Consistent Videos with Image Diffusion Model](https://mlsanity.com/paper/2308.09091/) (2023)
- [Cinematic Behavior Transfer via NeRF-based Differentiable Filming](https://mlsanity.com/paper/2311.17754/) (2023)
- [Transfer Learning for Pose Estimation of Illustrated Characters](https://mlsanity.com/paper/2108.01819/) (2021)
- [Multi-scale Attention Guided Pose Transfer](https://mlsanity.com/paper/2202.06777/) (2022)
- [PoseExaminer: Automated Testing of Out-of-Distribution Robustness in Human Pose and Shape Estimation](https://mlsanity.com/paper/2303.07337/) (2023)
- [VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation](https://mlsanity.com/paper/2210.06028/) (2022)
- [Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures](https://mlsanity.com/paper/2210.01887/) (2022)
- [Open-World Pose Transfer via Sequential Test-Time Adaption](https://mlsanity.com/paper/2303.10945/) (2023)
- [Human Pose Descriptions and Subject-Focused Attention for Improved Zero-Shot Transfer in Human-Centric Classification Tasks](https://mlsanity.com/paper/2403.06904/) (2024)
- [Human Pose Transfer with Augmented Disentangled Feature Consistency](https://mlsanity.com/paper/2107.10984/) (2021)

## Built on by (most cited first, 1 of 1)

- [Synthetic Human Action Video Data Generation with Pose Transfer](https://mlsanity.com/paper/2506.09411/) (2025)

## Abstract (arXiv)

Recent pose-transfer methods aim to generate temporally consistent and fully controllable videos of human action where the motion from a reference video is reenacted by a new identity. We evaluate three state-of-the-art pose-transfer methods -- AnimateAnyone, MagicAnimate, and ExAvatar -- by generating videos with actions and identities outside the training distribution and conducting a participant study about the quality of these videos. In a controlled environment of 20 distinct human actions, we find that participants, presented with the pose-transferred videos, correctly identify the desired action only 42.92% of the time. Moreover, the participants find the actions in the generated videos consistent with the reference (source) videos only 36.46% of the time. These results vary by method: participants find the splatting-based ExAvatar more consistent and photorealistic than the diffusion-based AnimateAnyone and MagicAnimate.