AutoVFX：基于自然语言指令的物理逼真视频编辑
AutoVFX: Physically Realistic Video Editing from Natural Language Instructions
AI总结：
AutoVFX提出一个框架，结合神经场景建模、LLM代码生成和物理模拟，从单个视频和自然语言指令自动生成物理逼真的动态VFX效果，实验证明其在生成质量、指令对齐、编辑多样性和物理合理性上大幅超越现有方法。
摘要：
现代视觉特效（VFX）软件使得技艺精湛的艺术家能够创作出几乎任何事物的图像。然而，创作过程仍然费力、复杂，并且对日常用户来说基本上难以触及。在这项工作中，我们提出了AutoVFX，一个能够从单个视频和自然语言指令自动创建逼真且动态的VFX视频的框架。通过精心整合神经场景建模、基于LLM的代码生成和物理模拟，AutoVFX能够提供物理上合理、照片级逼真的编辑效果，这些效果可以直接通过自然语言指令进行控制。我们进行了广泛的实验，以验证AutoVFX在多种视频和指令上的有效性。定量和定性结果表明，AutoVFX在生成质量、指令对齐、编辑多样性和物理合理性方面大幅优于所有竞争方法。
英文摘要：
Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and largely inaccessible to everyday users. In this work, we present AutoVFX, a framework that automatically creates realistic and dynamic VFX videos from a single video and natural language instructions. By carefully integrating neural scene modeling, LLM-based code generation, and physical simulation, AutoVFX is able to provide physically-grounded, photorealistic editing effects that can be controlled directly using natural language instructions. We conduct extensive experiments to validate AutoVFX's efficacy across a diverse spectrum of videos and instructions. Quantitative and qualitative results suggest that AutoVFX outperforms all competing methods by a large margin in generative quality, instruction alignment, editing versatility, and physical plausibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
