# SLoMo: A General System for Legged Robot Motion Imitation from Casual Videos

arXiv: https://arxiv.org/abs/2304.14389 · HTML: https://mlsanity.com/paper/2304.14389/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2023.

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

- [Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation](https://mlsanity.com/paper/2403.04436/) (2024)
- [Uni4D: Unifying Visual Foundation Models for 4D Modeling from a Single Video](https://mlsanity.com/paper/2503.21761/) (2025)
- [Visually Robust Adversarial Imitation Learning from Videos with Contrastive Learning](https://mlsanity.com/paper/2407.12792/) (2024)
- [DecAP: Decaying Action Priors for Accelerated Imitation Learning of Torque-Based Legged Locomotion Policies](https://mlsanity.com/paper/2310.05714/) (2023)
- [I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning](https://mlsanity.com/paper/2405.08726/) (2024)
- [One-shot Video Imitation via Parameterized Symbolic Abstraction Graphs](https://mlsanity.com/paper/2408.12674/) (2024)
- [Language-Model-Assisted Bi-Level Programming for Reward Learning from Internet Videos](https://mlsanity.com/paper/2410.09286/) (2024)
- [Reinforcement Learning from Wild Animal Videos](https://mlsanity.com/paper/2412.04273/) (2024)
- [Beyond Domain Randomization: Event-Inspired Perception for Visually Robust Adversarial Imitation from Videos](https://mlsanity.com/paper/2505.18899/) (2025)
- [Periodic Skill Discovery](https://mlsanity.com/paper/2511.03187/) (2025)
- [EMP: Executable Motion Prior for Humanoid Robot Standing Upper-body Motion Imitation](https://mlsanity.com/paper/2507.15649/) (2025)
- [CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model](https://mlsanity.com/paper/2610.16737/) (2026)

## Abstract (arXiv)

We present SLoMo: a first-of-its-kind framework for transferring skilled motions from casually captured "in the wild" video footage of humans and animals to legged robots. SLoMo works in three stages: 1) synthesize a physically plausible reconstructed key-point trajectory from monocular videos; 2) optimize a dynamically feasible reference trajectory for the robot offline that includes body and foot motion, as well as contact sequences that closely tracks the key points; 3) track the reference trajectory online using a general-purpose model-predictive controller on robot hardware. Traditional motion imitation for legged motor skills often requires expert animators, collaborative demonstrations, and/or expensive motion capture equipment, all of which limits scalability. Instead, SLoMo only relies on easy-to-obtain monocular video footage, readily available in online repositories such as YouTube. It converts videos into motion primitives that can be executed reliably by real-world robots. We demonstrate our approach by transferring the motions of cats, dogs, and humans to example robots including a quadruped (on hardware) and a humanoid (in simulation). To the best knowledge of the authors, this is the first attempt at a general-purpose motion transfer framework that imitates animal and human motions on legged robots directly from casual videos without artificial markers or labels.