Large language models (LLMs) have unified diverse linguistic tasks within a single framework, yet such unification remains unexplored in human motion generation. Existing methods are confined to isolated tasks, limiting flexibility for free-form and omni-objective generation. To address this, we propose OmniMoGen, a unified framework that enables versatile motion generation through interleaved text-motion instructions. Built upon a concise RVQ-VAE and transformer architecture, OmniMoGen supports end-to-end instruction-driven motion generation. We construct X2Mo, a large-scale dataset of over 137K interleaved text-motion instructions, and introduce AnyContext, a benchmark for evaluating interleaved motion generation. Experiments show that OmniMoGen achieves state-of-the-art performance on text-to-motion, motion editing, and AnyContext, exhibiting emerging capabilities such as compositional editing, self-reflective generation, and knowledge-informed generation. These results mark a step toward the next intelligent motion generation. Project Page: https://OmniMoGen.github.io/.
@article{arxiv.2512.19159,
title = {OmniMoGen: Unifying Human Motion Generation via Learning from Interleaved Text-Motion Instructions},
author = {Wendong Bu and Kaihang Pan and Yuze Lin and Jiacheng Li and Kai Shen and Wenqiao Zhang and Juncheng Li and Jun Xiao and Siliang Tang},
journal= {arXiv preprint arXiv:2512.19159},
year = {2025}
}