探索视频生成中的物理认知演进：一项综述

Exploring the Evolution of Physics Cognition in Video Generation: A Survey

March 27, 2025

作者: Minghui Lin, Xiang Wang, Yishan Wang, Shu Wang, Fengqi Dai, Pengxiang Ding, Cunxiang Wang, Zhengrong Zuo, Nong Sang, Siteng Huang, Donglin Wang

cs.AI

摘要

近期，视频生成领域取得了显著进展，尤其是在扩散模型快速发展的推动下。然而，其在物理认知方面的不足逐渐引起广泛关注——生成内容常违背基本物理定律，陷入“视觉逼真但物理荒谬”的困境。研究者们日益认识到物理保真度在视频生成中的重要性，并尝试将运动表征及物理知识等启发式物理认知融入生成系统，以模拟现实世界的动态场景。鉴于该领域缺乏系统性综述，本文旨在通过全面总结架构设计及其应用来填补这一空白。具体而言，我们从认知科学的角度探讨并梳理了视频生成中物理认知的演进过程，同时提出了一个三层分类体系：1）面向生成的基础图式感知，2）面向生成的物理知识被动认知，3）面向世界模拟的主动认知，涵盖了最新方法、经典范式及基准测试。随后，我们强调了该领域固有的关键挑战，并勾勒出未来研究的潜在路径，为学术界与工业界的讨论前沿贡献力量。通过结构化回顾与跨学科分析，本综述旨在为开发可解释、可控且物理一致的视频生成范式提供方向性指导，从而推动生成模型从“视觉模仿”阶段迈向“类人物理理解”的新阶段。

English

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''.