title: CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation
summary: Crowd Motion Generation is essential in entertainment industries such as
  animation and games as well as in strategic fields like urban simulation and
  planning. This new task requires an intricate integration of control and
  generation to realistically synthesize crowd dynamics under specific spatial
  and semantic constraints, whose challenges are yet to be fully explored. On the
  one hand, existing human motion generation models typically focus on individual
  behaviors, neglecting the complexities of collective behaviors. On the other
  hand, recent methods for multi-person motion generation depend heavily on
  pre-defined scenarios and are limited to a fixed, small number of inter-person
  interactions, thus hampering their practicality. To overcome these challenges,
  we introduce CrowdMoGen, a zero-shot text-driven framework that harnesses the
  power of Large Language Model (LLM) to incorporate the collective intelligence
  into the motion generation framework as guidance, thereby enabling
  generalizable planning and generation of crowd motions without paired training
  data. Our framework consists of two key components: 1) Crowd Scene Planner that
  learns to coordinate motions and dynamics according to specific scene contexts
  or introduced perturbations, and 2) Collective Motion Generator that
  efficiently synthesizes the required collective motions based on the holistic
  plans. Extensive quantitative and qualitative experiments have validated the
  effectiveness of our framework, which not only fills a critical gap by
  providing scalable and generalizable solutions for Crowd Motion Generation task
  but also achieves high levels of realism and flexibility.
paper_authors._id: 668fd089a322a394ce804413
paper_authors.name: Xinying Guo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-11T13:45:37.435Z
paper_authors._id: 64e2f06bd5e75575cd5643cd
paper_authors.avatarUrl: /avatars/6e87a832367c47e7feea2fb455ef7e07.svg
paper_authors.fullname: Guo Xinying
paper_authors.type: user
paper_authors.user: gxyes
paper_authors._id: 668fd089a322a394ce804414
paper_authors.name: Mingyuan Zhang
paper_authors._id: 668fd089a322a394ce804415
paper_authors.name: Haozhe Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-22T12:48:51.152Z
paper_authors._id: 63f47b5321eb234ab739e91a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63f47b5321eb234ab739e91a/vWfFNVtMkHl8gieha5PPd.jpeg
paper_authors.fullname: Haozhe Xie
paper_authors.type: user
paper_authors.user: hzxie
paper_authors._id: 668fd089a322a394ce804416
paper_authors.name: Chenyang Gu
paper_authors._id: 668fd089a322a394ce804417
paper_authors.name: Ziwei Liu
paper_title: CrowdMoGen: Zero-Shot Text-Driven Collective Motion Generation
paper_summary: Crowd Motion Generation is essential in entertainment industries such as
  animation and games as well as in strategic fields like urban simulation and
  planning. This new task requires an intricate integration of control and
  generation to realistically synthesize crowd dynamics under specific spatial
  and semantic constraints, whose challenges are yet to be fully explored. On the
  one hand, existing human motion generation models typically focus on individual
  behaviors, neglecting the complexities of collective behaviors. On the other
  hand, recent methods for multi-person motion generation depend heavily on
  pre-defined scenarios and are limited to a fixed, small number of inter-person
  interactions, thus hampering their practicality. To overcome these challenges,
  we introduce CrowdMoGen, a zero-shot text-driven framework that harnesses the
  power of Large Language Model (LLM) to incorporate the collective intelligence
  into the motion generation framework as guidance, thereby enabling
  generalizable planning and generation of crowd motions without paired training
  data. Our framework consists of two key components: 1) Crowd Scene Planner that
  learns to coordinate motions and dynamics according to specific scene contexts
  or introduced perturbations, and 2) Collective Motion Generator that
  efficiently synthesizes the required collective motions based on the holistic
  plans. Extensive quantitative and qualitative experiments have validated the
  effectiveness of our framework, which not only fills a critical gap by
  providing scalable and generalizable solutions for Crowd Motion Generation task
  but also achieves high levels of realism and flexibility.
paper_ai_summary: CrowdMoGen is a zero-shot text-driven framework that uses LLMs to guide crowd motion generation, achieving realism and flexibility without paired training data through a planner and a generator.
paper_ai_keywords: Large Language Model (LLM) Crowd Scene Planner Collective Motion Generator