title: Exploring the Evolution of Physics Cognition in Video Generation: A
    Survey
summary: Recent advancements in video generation have witnessed significant progress,
  especially with the rapid advancement of diffusion models. Despite this, their
  deficiencies in physical cognition have gradually received widespread attention
  - generated content often violates the fundamental laws of physics, falling
  into the dilemma of ''visual realism but physical absurdity". Researchers began
  to increasingly recognize the importance of physical fidelity in video
  generation and attempted to integrate heuristic physical cognition such as
  motion representations and physical knowledge into generative systems to
  simulate real-world dynamic scenarios. Considering the lack of a systematic
  overview in this field, this survey aims to provide a comprehensive summary of
  architecture designs and their applications to fill this gap. Specifically, we
  discuss and organize the evolutionary process of physical cognition in video
  generation from a cognitive science perspective, while proposing a three-tier
  taxonomy: 1) basic schema perception for generation, 2) passive cognition of
  physical knowledge for generation, and 3) active cognition for world
  simulation, encompassing state-of-the-art methods, classical paradigms, and
  benchmarks. Subsequently, we emphasize the inherent key challenges in this
  domain and delineate potential pathways for future research, contributing to
  advancing the frontiers of discussion in both academia and industry. Through
  structured review and interdisciplinary analysis, this survey aims to provide
  directional guidance for developing interpretable, controllable, and physically
  consistent video generation paradigms, thereby propelling generative models
  from the stage of ''visual mimicry'' towards a new phase of ''human-like
  physical comprehension''.
paper_authors._id: 67e60da88decdc7da4bf69a9
paper_authors.name: Minghui Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:01:37.704Z
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paper_authors.avatarUrl: /avatars/97a5ba0d0422f04e396399da1b74e8d4.svg
paper_authors.fullname: Minghui Lin
paper_authors.type: user
paper_authors.user: minnielin
paper_authors._id: 67e60da88decdc7da4bf69aa
paper_authors.name: Xiang Wang
paper_authors._id: 67e60da88decdc7da4bf69ab
paper_authors.name: Yishan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:01:51.882Z
paper_authors._id: 65dcaf16287a93e081d9c2f0
paper_authors.avatarUrl: /avatars/2db4e25c6924461abb5634f8ffd1ee87.svg
paper_authors.fullname: Yishanwang
paper_authors.type: user
paper_authors.user: yishanwang
paper_authors._id: 67e60da88decdc7da4bf69ac
paper_authors.name: Shu Wang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-28T02:47:05.703Z
paper_authors._id: 65fd82762bf2cd20ddaa193f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/yBYbWp_mT7UusYdkqtAvw.png
paper_authors.fullname: Siteng Huang
paper_authors.type: user
paper_authors.user: huangsiteng
paper_authors._id: 67e60da88decdc7da4bf69ad
paper_authors.name: Fengqi Dai
paper_authors._id: 67e60da88decdc7da4bf69ae
paper_authors.name: Pengxiang Ding
paper_authors._id: 67e60da88decdc7da4bf69af
paper_authors.name: Cunxiang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:02:15.357Z
paper_authors._id: 65eaf755ab0a6a90da55ab58
paper_authors.avatarUrl: /avatars/a46890a9d067a913513edf3759f12c85.svg
paper_authors.fullname: Cunxiang Wang
paper_authors.type: user
paper_authors.user: wangcunxiang
paper_authors._id: 67e60da88decdc7da4bf69b0
paper_authors.name: Zhengrong Zuo
paper_authors._id: 67e60da88decdc7da4bf69b1
paper_authors.name: Nong Sang
paper_authors._id: 67e60da88decdc7da4bf69b2
paper_authors.name: Siteng Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:02:43.147Z
paper_authors._id: 65fd82762bf2cd20ddaa193f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/yBYbWp_mT7UusYdkqtAvw.png
paper_authors.fullname: Siteng Huang
paper_authors.type: user
paper_authors.user: huangsiteng
paper_authors._id: 67e60da88decdc7da4bf69b3
paper_authors.name: Donglin Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T09:02:37.839Z
paper_authors._id: 67597be2f3cd6492d4162ef8
paper_authors.avatarUrl: /avatars/ba580c04b7057927d4a22dcb44c52400.svg
paper_authors.fullname: DONGLIN
paper_authors.type: user
paper_authors.user: wangdonglin130
paper_title: Exploring the Evolution of Physics Cognition in Video Generation: A
    Survey
paper_summary: Recent advancements in video generation have witnessed significant progress,
  especially with the rapid advancement of diffusion models. Despite this, their
  deficiencies in physical cognition have gradually received widespread attention
  - generated content often violates the fundamental laws of physics, falling
  into the dilemma of ''visual realism but physical absurdity". Researchers began
  to increasingly recognize the importance of physical fidelity in video
  generation and attempted to integrate heuristic physical cognition such as
  motion representations and physical knowledge into generative systems to
  simulate real-world dynamic scenarios. Considering the lack of a systematic
  overview in this field, this survey aims to provide a comprehensive summary of
  architecture designs and their applications to fill this gap. Specifically, we
  discuss and organize the evolutionary process of physical cognition in video
  generation from a cognitive science perspective, while proposing a three-tier
  taxonomy: 1) basic schema perception for generation, 2) passive cognition of
  physical knowledge for generation, and 3) active cognition for world
  simulation, encompassing state-of-the-art methods, classical paradigms, and
  benchmarks. Subsequently, we emphasize the inherent key challenges in this
  domain and delineate potential pathways for future research, contributing to
  advancing the frontiers of discussion in both academia and industry. Through
  structured review and interdisciplinary analysis, this survey aims to provide
  directional guidance for developing interpretable, controllable, and physically
  consistent video generation paradigms, thereby propelling generative models
  from the stage of ''visual mimicry'' towards a new phase of ''human-like
  physical comprehension''.
paper_ai_summary: This survey reviews the integration of physical cognition into video generation to improve realism while maintaining physical consistency, proposing a taxonomy for understanding the evolutionary process and challenges in this domain.
paper_ai_keywords: diffusion models physical cognition motion representations physical knowledge generative systems cognitive science schema perception passive cognition active cognition world simulation physical consistency