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<p>While recent advances in text-to-motion generation have shown promising results, they typically assume all individuals are grouped as a single unit. Scaling these methods to handle larger crowds and ensuring that individuals respond appropriately to specific events remains a significant challenge. This is primarily due to the complexities of scene planning–which involves organizing groups, planning their activities, and coordinating interactions–and controllable motion generation. In this paper, we present <b>CrowdMoGen </b>, the first zero-shot framework for collective motion generation, which effectively groups individuals and generates event-aligned motion sequences from text prompts. <b>1)</b> Being limited by the available datasets for training an effective scene planning module in a supervised manner, we instead propose a <b>crowd scene planner</b> that leverages pre-trained large language models (LLMs) to organize individuals into distinct groups. While LLMs offer high-level guidance for group divisions, they lack the low-level understanding of human motion. To address this, we further propose integrating an SMPL-based joint prior to generate context-appropriate activities, which consists of both joint trajectories and textual descriptions. <b>2)</b> Secondly, to incorporate the assigned activities into the generative network, we introduce a <b>collective motion generator</b> that integrates the activities into a transformer-based network in a joint-wise manner, maintaining the spatial constraints during the multi-step denoising process. Extensive experiments demonstrate that CrowdMoGen significantly outperforms previous approaches, delivering realistic, event-driven motion sequences that are spatially coherent. As the first framework of collective motion generation, CrowdMoGen has the potential to advance applications in urban simulation, crowd planning, and other large-scale interactive environments.</p>

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CrowdMoGen: Event-Driven Collective Human Motion Generation

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Yukang Cao,
  • Xinying Guo,
  • Mingyuan Zhang,
  • Haozhe Xie,
  • Chenyang Gu,
  • Ziwei Liu

摘要

While recent advances in text-to-motion generation have shown promising results, they typically assume all individuals are grouped as a single unit. Scaling these methods to handle larger crowds and ensuring that individuals respond appropriately to specific events remains a significant challenge. This is primarily due to the complexities of scene planning–which involves organizing groups, planning their activities, and coordinating interactions–and controllable motion generation. In this paper, we present CrowdMoGen , the first zero-shot framework for collective motion generation, which effectively groups individuals and generates event-aligned motion sequences from text prompts. 1) Being limited by the available datasets for training an effective scene planning module in a supervised manner, we instead propose a crowd scene planner that leverages pre-trained large language models (LLMs) to organize individuals into distinct groups. While LLMs offer high-level guidance for group divisions, they lack the low-level understanding of human motion. To address this, we further propose integrating an SMPL-based joint prior to generate context-appropriate activities, which consists of both joint trajectories and textual descriptions. 2) Secondly, to incorporate the assigned activities into the generative network, we introduce a collective motion generator that integrates the activities into a transformer-based network in a joint-wise manner, maintaining the spatial constraints during the multi-step denoising process. Extensive experiments demonstrate that CrowdMoGen significantly outperforms previous approaches, delivering realistic, event-driven motion sequences that are spatially coherent. As the first framework of collective motion generation, CrowdMoGen has the potential to advance applications in urban simulation, crowd planning, and other large-scale interactive environments.