# ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters

arXiv: https://arxiv.org/abs/2305.14792 · HTML: https://mlsanity.com/paper/2305.14792/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2023.

## Built on by (most cited first, 12 of 12)

- [WalkTheDog: Cross-Morphology Motion Alignment via Phase Manifolds](https://mlsanity.com/paper/2407.18946/) (2024)
- [Learning to Transfer Human Hand Skills for Robot Manipulations](https://mlsanity.com/paper/2501.04169/) (2025)
- [AAMDM: Accelerated Auto-regressive Motion Diffusion Model](https://mlsanity.com/paper/2401.06146/) (2023)
- [Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2404.17687/) (2024)
- [Reinforcement Learning from Wild Animal Videos](https://mlsanity.com/paper/2412.04273/) (2024)
- [MoReFlow: Motion Retargeting Learning through Unsupervised Flow Matching](https://mlsanity.com/paper/2509.25600/) (2025)
- [ReActor: Reinforcement Learning for Physics-Aware Motion Retargeting](https://mlsanity.com/paper/2605.06593/) (2026)
- [Learning to enhance multi-legged robot on rugged landscapes](https://mlsanity.com/paper/2409.09473/) (2024)
- [ELMO: Enhanced Real-time LiDAR Motion Capture through Upsampling](https://mlsanity.com/paper/2410.06963/) (2024)
- [MimiCAT: Mimic with Correspondence-Aware Cascade-Transformer for Category-Free 3D Pose Transfer](https://mlsanity.com/paper/2511.18370/) (2025)
- [Two2Four: Generative Quadruped Puppeteering from Human Motion](https://mlsanity.com/paper/2607.26108/) (2026)
- [Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of Generative Motion Models](https://mlsanity.com/paper/2609.37297/) (2026)

## Abstract (arXiv)

Motion retargeting is a promising approach for generating natural and compelling animations for nonhuman characters. However, it is challenging to translate human movements into semantically equivalent motions for target characters with different morphologies due to the ambiguous nature of the problem. This work presents a novel learning-based motion retargeting framework, Adversarial Correspondence Embedding (ACE), to retarget human motions onto target characters with different body dimensions and structures. Our framework is designed to produce natural and feasible robot motions by leveraging generative-adversarial networks (GANs) while preserving high-level motion semantics by introducing an additional feature loss. In addition, we pretrain a robot motion prior that can be controlled in a latent embedding space and seek to establish a compact correspondence. We demonstrate that the proposed framework can produce retargeted motions for three different characters -- a quadrupedal robot with a manipulator, a crab character, and a wheeled manipulator. We further validate the design choices of our framework by conducting baseline comparisons and a user study. We also showcase sim-to-real transfer of the retargeted motions by transferring them to a real Spot robot.