CrowdMoGen：零样本文本驱动的群体运动生成
CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation
AI总结：
本文提出零样本框架 CrowdMoGen，利用 LLM 场景规划、SMPL 关节先验和逐关节 transformer 生成器，从文本生成与事件对齐且空间连贯的群体运动。
摘要：
尽管文本到运动生成的最新进展已展现出可喜成果，但这些方法通常假设所有个体都被归为一个单一单元。将这些方法扩展以处理更大规模人群，并确保个体对特定事件作出适当响应，仍然是一项重大挑战。这主要源于场景规划（包括组织群体、规划其活动并协调交互）以及可控运动生成的复杂性。本文提出 CrowdMoGen，这是首个用于群体运动生成的零样本框架，能够有效地对个体进行分组，并根据文本提示生成与事件对齐的运动序列。1）由于可用于以监督方式训练有效场景规划模块的数据集有限，我们转而提出一种人群场景规划器，利用预训练的大型语言模型（LLM）将个体组织成不同群体。尽管 LLM 为群体划分提供高层指导，但它们缺乏对人体运动的低层理解。为解决这一问题，我们进一步提出集成基于 SMPL 的关节先验，以生成符合情境的活动，该先验同时包含关节轨迹和文本描述。2）其次，为将分配的活动纳入生成网络，我们引入一种群体运动生成器，以逐关节方式将活动集成到基于 transformer 的网络中，并在多步去噪过程中维持空间约束。大量实验表明，CrowdMoGen 显著优于以往方法，可生成真实、由事件驱动且空间连贯的运动序列。作为首个群体运动生成框架，CrowdMoGen 有潜力推动城市模拟、人群规划及其他大规模交互环境中的应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
