title: A Survey of Interactive Generative Video
summary: Interactive Generative Video (IGV) has emerged as a crucial technology in
  response to the growing demand for high-quality, interactive video content
  across various domains. In this paper, we define IGV as a technology that
  combines generative capabilities to produce diverse high-quality video content
  with interactive features that enable user engagement through control signals
  and responsive feedback. We survey the current landscape of IGV applications,
  focusing on three major domains: 1) gaming, where IGV enables infinite
  exploration in virtual worlds; 2) embodied AI, where IGV serves as a
  physics-aware environment synthesizer for training agents in multimodal
  interaction with dynamically evolving scenes; and 3) autonomous driving, where
  IGV provides closed-loop simulation capabilities for safety-critical testing
  and validation. To guide future development, we propose a comprehensive
  framework that decomposes an ideal IGV system into five essential modules:
  Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we
  systematically analyze the technical challenges and future directions in
  realizing each component for an ideal IGV system, such as achieving real-time
  generation, enabling open-domain control, maintaining long-term coherence,
  simulating accurate physics, and integrating causal reasoning. We believe that
  this systematic analysis will facilitate future research and development in the
  field of IGV, ultimately advancing the technology toward more sophisticated and
  practical applications.
paper_authors._id: 681441e64d6a681c7c840b1f
paper_authors.name: Jiwen Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:43:35.508Z
paper_authors._id: 64105a6d14215c0775dfdd14
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64105a6d14215c0775dfdd14/-VX-cUYOLjHIg7QnWhRGG.jpeg
paper_authors.fullname: Jiwen Yu
paper_authors.type: user
paper_authors.user: VictorYuki
paper_authors._id: 681441e64d6a681c7c840b20
paper_authors.name: Yiran Qin
paper_authors._id: 681441e64d6a681c7c840b21
paper_authors.name: Haoxuan Che
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:43:15.002Z
paper_authors._id: 652404d0050781c16f1c51b0
paper_authors.avatarUrl: /avatars/4ad62f2c65406dd0af36c6d0697ae599.svg
paper_authors.fullname: Haoxuan Che
paper_authors.type: user
paper_authors.user: chehx
paper_authors._id: 681441e64d6a681c7c840b22
paper_authors.name: Quande Liu
paper_authors._id: 681441e64d6a681c7c840b23
paper_authors.name: Xintao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:42:59.735Z
paper_authors._id: 60e272ca6c78a8c122b12127
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60e272ca6c78a8c122b12127/xldEGBzGrU-bX6IwAw0Ie.jpeg
paper_authors.fullname: Xintao Wang
paper_authors.type: user
paper_authors.user: Xintao
paper_authors._id: 681441e64d6a681c7c840b24
paper_authors.name: Pengfei Wan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-04T10:05:13.156Z
paper_authors._id: 662f93942510ef5735d7ad00
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/662f93942510ef5735d7ad00/ZIDIPm63sncIHFTT5b0uR.png
paper_authors.fullname: magicwpf
paper_authors.type: user
paper_authors.user: magicwpf
paper_authors._id: 681441e64d6a681c7c840b25
paper_authors.name: Di Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:42:52.723Z
paper_authors._id: 644c8324f02250233d0d67d9
paper_authors.avatarUrl: /avatars/feb39d281457c1750f3eada3c060a23e.svg
paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: dizhang
paper_authors._id: 681441e64d6a681c7c840b26
paper_authors.name: Kun Gai
paper_authors._id: 681441e64d6a681c7c840b27
paper_authors.name: Hao Chen
paper_authors._id: 681441e64d6a681c7c840b28
paper_authors.name: Xihui Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:43:44.271Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_title: A Survey of Interactive Generative Video
paper_summary: Interactive Generative Video (IGV) has emerged as a crucial technology in
  response to the growing demand for high-quality, interactive video content
  across various domains. In this paper, we define IGV as a technology that
  combines generative capabilities to produce diverse high-quality video content
  with interactive features that enable user engagement through control signals
  and responsive feedback. We survey the current landscape of IGV applications,
  focusing on three major domains: 1) gaming, where IGV enables infinite
  exploration in virtual worlds; 2) embodied AI, where IGV serves as a
  physics-aware environment synthesizer for training agents in multimodal
  interaction with dynamically evolving scenes; and 3) autonomous driving, where
  IGV provides closed-loop simulation capabilities for safety-critical testing
  and validation. To guide future development, we propose a comprehensive
  framework that decomposes an ideal IGV system into five essential modules:
  Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we
  systematically analyze the technical challenges and future directions in
  realizing each component for an ideal IGV system, such as achieving real-time
  generation, enabling open-domain control, maintaining long-term coherence,
  simulating accurate physics, and integrating causal reasoning. We believe that
  this systematic analysis will facilitate future research and development in the
  field of IGV, ultimately advancing the technology toward more sophisticated and
  practical applications.
paper_ai_summary: Interactive Generative Video (IGV) combines generative capabilities with interactive features to enable diverse applications across gaming, embodied AI, and autonomous driving, focusing on challenges such as real-time generation, control, memory, dynamics, and intelligence.
paper_ai_keywords: Interactive Generative Video (IGV) generative capabilities interactive features user engagement control signals responsive feedback virtual worlds embodied AI physics-aware environment synthesizer multimodal interaction dynamically evolving scenes autonomous driving closed-loop simulation real-time generation open-domain control long-term coherence accurate physics causal reasoning