# Pose-to-Motion: Cross-Domain Motion Retargeting with Pose Prior

arXiv: https://arxiv.org/abs/2310.20249 · HTML: https://mlsanity.com/paper/2310.20249/

## Key facts

- Cited by 8 papers in the map (data through 7 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 20 of 20)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [MagicPony: Learning Articulated 3D Animals in the Wild](https://mlsanity.com/paper/2211.12497/) (2022)
- [Unpaired Motion Style Transfer from Video to Animation](https://mlsanity.com/paper/2005.05751/) (2020)
- [Neural Kinematic Networks for Unsupervised Motion Retargetting](https://mlsanity.com/paper/1804.05653/) (2018)
- [Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation](https://mlsanity.com/paper/2203.15720/) (2022)
- [PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time](https://mlsanity.com/paper/2008.08880/) (2020)
- [Adversarial Parametric Pose Prior](https://mlsanity.com/paper/2112.04203/) (2021)
- [Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry](https://mlsanity.com/paper/2303.08658/) (2023)
- [Deep Video-Based Performance Cloning](https://mlsanity.com/paper/1808.06847/) (2018)
- [Contact-Aware Retargeting of Skinned Motion](https://mlsanity.com/paper/2109.07431/) (2021)
- [Skeleton-free Pose Transfer for Stylized 3D Characters](https://mlsanity.com/paper/2208.00790/) (2022)
- [GANimator: Neural Motion Synthesis from a Single Sequence](https://mlsanity.com/paper/2205.02625/) (2022)
- [Dance Style Transfer with Cross-modal Transformer](https://mlsanity.com/paper/2208.09406/) (2022)
- [Physics-based Motion Retargeting from Sparse Inputs](https://mlsanity.com/paper/2307.01938/) (2023)
- [Pose Representations for Deep Skeletal Animation](https://mlsanity.com/paper/2111.13907/) (2021)

## Built on by (most cited first, 8 of 8)

- [Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence](https://mlsanity.com/paper/2508.13139/) (2025)
- [How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects](https://mlsanity.com/paper/2503.04257/) (2025)
- [MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation](https://mlsanity.com/paper/2505.10810/) (2025)
- [A Scalable Whole-body Motion Transfer via Implicit Kinodynamic Motion Retargeting](https://mlsanity.com/paper/2509.15443/) (2025)
- [MimiCAT: Mimic with Correspondence-Aware Cascade-Transformer for Category-Free 3D Pose Transfer](https://mlsanity.com/paper/2511.18370/) (2025)
- [TopoCap: Learning Topology-Agnostic Motion Priors for Monocular Video-to-Animation](https://mlsanity.com/paper/2606.12153/) (2026)
- [Two2Four: Generative Quadruped Puppeteering from Human Motion](https://mlsanity.com/paper/2607.26108/) (2026)
- [STyMo: Fast and Controllable Few-Shot Motion Style Transfer](https://mlsanity.com/paper/2609.04500/) (2026)

## Abstract (arXiv)

Creating believable motions for various characters has long been a goal in computer graphics. Current learning-based motion synthesis methods depend on extensive motion datasets, which are often challenging, if not impossible, to obtain. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extracted from images using recent advancements in computer vision. In this paper, we utilize this alternative data source and introduce a neural motion synthesis approach through retargeting. Our method generates plausible motions for characters that have only pose data by transferring motion from an existing motion capture dataset of another character, which can have drastically different skeletons. Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist-created poses to noisy poses estimated directly from images. Additionally, a conducted user study indicated that a majority of participants found our retargeted motion to be more enjoyable to watch, more lifelike in appearance, and exhibiting fewer artifacts. Project page: https://cyanzhao42.github.io/pose2motion