# OMG: Omni-Modal Motion Generation for Generalist Humanoid Control

arXiv: https://arxiv.org/abs/2606.10340 · HTML: https://mlsanity.com/paper/2606.10340/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2026.

## Built on by (most cited first, 2 of 2)

- [SocialHumanoid: Towards Expressive Humanoid Behavior via One-Step Co-Speech Motion Generation](https://mlsanity.com/paper/2609.33311/) (2026)
- [ECHO-G: Embodied Co-speech Humanoid mOtion Generation](https://mlsanity.com/paper/2609.39575/) (2026)

## Abstract (arXiv)

Humanoid whole-body control has made significant progress in recent years, yet existing approaches remain limited to few-skill policies with heavy reward engineering, or motion trackers that are difficult to extend to new input modalities. We argue that the key to general-purpose humanoid control is to build a scalable brain, a module capable of reasoning with diverse conditioning modalities, atop a reactive motion tracking cerebellum, mirroring the hierarchical structure of biological motor systems. Two challenges arise in realizing this vision: acquiring a vast amount of high-quality data to achieve general purpose control, and equipping the generator with the capability to condition on compositional, extensible multi-modal inputs. We present OMG, which addresses these challenges with a meticulous data curation, filtering and labeling pipeline, as well as a diffusion-based motion generation backbone that conditions on language, audio, and human reference motions. Extensive experiments validate OMG as an omni-modal whole-body controller exhibiting state-of-the-art performance, model scaling behavior and efficient adaptation to new distributions and modalities, marking a concrete step toward foundation models for humanoid robots.