标题： 运动感知视频生成模型 显示英文标题

标题： Motion aware video generative model

摘要： 基于扩散的视频生成技术的最新进展在视觉内容和语义连贯性方面取得了前所未有的质量。 然而，当前的方法主要依赖于从大量数据集中进行统计学习，而没有显式地对运动的底层物理特性进行建模，导致了细微但可感知的非物理伪影，从而降低了生成视频的真实感。 本文介绍了一种基于物理学的频域方法，以增强生成视频的物理合理性。 我们首先对各种物理运动（平移、旋转、缩放）在频域中的特征进行了系统分析，揭示了每种运动类型都表现出独特且可识别的频谱特征。 在此理论基础上，我们提出了两个互补的组件：(1) 一种物理运动损失函数，用于量化并优化生成视频与理想频域运动模式的一致性；(2) 一个频域增强模块，通过零初始化策略逐步学习调整视频特征以符合物理运动约束，同时保留原始网络功能。 在多种视频扩散架构上的实验表明，我们的方法显著提升了运动质量和物理合理性，而不会损害视觉质量和语义一致性。 我们的频域物理运动框架能够有效适用于不同的视频生成架构，为将物理约束整合到基于深度学习的视频合成流水线中提供了一种原则性的方法。 这项工作旨在建立数据驱动模型与基于物理的运动模型之间的联系。 显示英文摘要

摘要： Recent advances in diffusion-based video generation have yielded unprecedented quality in visual content and semantic coherence. However, current approaches predominantly rely on statistical learning from vast datasets without explicitly modeling the underlying physics of motion, resulting in subtle yet perceptible non-physical artifacts that diminish the realism of generated videos. This paper introduces a physics-informed frequency domain approach to enhance the physical plausibility of generated videos. We first conduct a systematic analysis of the frequency-domain characteristics of diverse physical motions (translation, rotation, scaling), revealing that each motion type exhibits distinctive and identifiable spectral signatures. Building on this theoretical foundation, we propose two complementary components: (1) a physical motion loss function that quantifies and optimizes the conformity of generated videos to ideal frequency-domain motion patterns, and (2) a frequency domain enhancement module that progressively learns to adjust video features to conform to physical motion constraints while preserving original network functionality through a zero-initialization strategy. Experiments across multiple video diffusion architectures demonstrate that our approach significantly enhances motion quality and physical plausibility without compromising visual quality or semantic alignment. Our frequency-domain physical motion framework generalizes effectively across different video generation architectures, offering a principled approach to incorporating physical constraints into deep learning-based video synthesis pipelines. This work seeks to establish connections between data-driven models and physics-based motion models.