Summary
Understanding and creating human movements in AI is tough because current models treat motion like just another kind of language, which limits how well the AI learns. The authors made a new model called Open-UniMo that treats motion and language equally by giving both their own tokens in a shared space. This helps the AI better understand and generate long motion sequences without making many errors. They also created a new way to connect language and motion step-by-step, improving how well the AI links words and movements. Their tests show this method works better than existing ones for both describing motions in words and creating motions from descriptions.
What this means in practice
- •For robotics engineers: Enable robots to better understand and generate human-like motions from natural language instructions in dynamic, real-world settings.
- •For game developers: Create more realistic character animations by generating motions from textual descriptions with improved accuracy and consistency.
Authors
Guocun Wang, Kenkun Liu, Guorui Song, Jing Lin, Zhe Huang, Luyuan Zhang, Dake Zhong, Choo Sin Wai, Xiaoguang Han, Haoqian Wang
Abstract
Unified motion generation and understanding is crucial for embodied AI systems that can both synthesize and interpret human actions in open-world environments. Existing motion-language models often treat motion as an auxiliary modality of a language model, leading to text-dominated representations and limited cross-modal interaction. Moreover, the next-token prediction paradigm is not naturally suited to long motion sequences, where autoregressive generation may accumulate prediction errors. To address these challenges, we propose Open-UniMo, a unified Large Motion-Language Model (LMLM) trained on million-scale open-world motion-language data. Open-UniMo promotes modality parity by extending Qwen's vocabulary of about 150K text tokens with 64K motion tokens, enabling motion and language to share a unified token space. We further introduce motion-consistent Chain-of-Thought reasoning as an intermediate representation to bridge language semantics and motion dynamics. Open-UniMo is trained with a two-stage pipeline, where supervised fine-tuning establishes CoT-guided bidirectional motion-language mapping and Group Relative Policy Optimization (GRPO) improves semantic alignment while mitigating cumulative errors in autoregressive motion-token generation. To support comprehensive evaluation, we propose Open-MoBench, a VLM-guided benchmark for assessing text-to-motion (T2M) generation, motion-to-text (M2T) understanding, and bidirectional consistency. Extensive experiments show that Open-UniMo achieves state-of-the-art performance on both conventional metrics and Open-MoBench. Furthermore, ablation studies reveal that M2T understanding is not primarily limited by motion-token vocabulary size; instead, coupling M2T with the learnable T2M generation path yields stronger cross-modal representations, demonstrating that generation can facilitate understanding in AR-based motion-language modeling.