VERTIGO：用于电影摄像机轨迹生成的视觉偏好优化
VERTIGO: Visual Preference Optimization for Cinematic Camera Trajectory Generation
AI总结：
本文提出VERTIGO，通过视觉偏好优化提升摄像机轨迹生成的质量，通过实时渲染和视觉语言模型优化，提高画面构图和视觉效果。
摘要：
电影摄像控制依赖导演和摄影师之间的紧密反馈循环，其中摄像机运动和构图不断被审查和优化。最近的生成摄像机系统能够产生多样化的、基于文本的轨迹，但缺乏这种'导演在环'机制，并且没有明确监督拍摄是否在视觉上可取。这导致了在分布内的摄像机运动但较差的构图、屏幕外的角色和不理想的视觉美学。本文介绍VERTIGO，首个用于摄像机轨迹生成器的视觉偏好优化框架。我们的框架利用实时图形引擎（Unity）从生成的摄像机运动中渲染2D视觉预览。一个经过电影精细调优的视觉语言模型随后使用我们提出的循环语义相似性机制对这些预览进行评分，这将渲染与文本提示对齐。此过程为训练后直接偏好优化（DPO）提供视觉偏好信号。定量评估和针对Unity渲染和基于扩散的摄像机到视频管道的用户研究显示，在条件符合性、构图质量和感知现实方面有持续的提升。值得注意的是，VERTIGO将屏幕外角色的比例从38%降低到几乎0%，同时保持摄像机运动的几何保真度。用户研究参与者进一步在构图、一致性、提示符合性和美学质量上更喜欢VERTIGO，确认了我们视觉偏好训练的感知优势。
英文摘要：
Cinematic camera control relies on a tight feedback loop between director and cinematographer, where camera motion and framing are continuously reviewed and refined. Recent generative camera systems can produce diverse, text-conditioned trajectories, but they lack this "director in the loop" and have no explicit supervision of whether a shot is visually desirable. This results in in-distribution camera motion but poor framing, off-screen characters, and undesirable visual aesthetics. In this paper, we introduce VERTIGO, the first framework for visual preference optimization of camera trajectory generators. Our framework leverages a real-time graphics engine (Unity) to render 2D visual previews from generated camera motion. A cinematically fine-tuned vision-language model then scores these previews using our proposed cyclic semantic similarity mechanism, which aligns renders with text prompts. This process provides the visual preference signals for Direct Preference Optimization (DPO) post-training. Both quantitative evaluations and user studies on Unity renders and diffusion-based Camera-to-Video pipelines show consistent gains in condition adherence, framing quality, and perceptual realism. Notably, VERTIGO reduces the character off-screen rate from 38% to nearly 0% while preserving the geometric fidelity of camera motion. User study participants further prefer VERTIGO over baselines across composition, consistency, prompt adherence, and aesthetic quality, confirming the perceptual benefits of our visual preference post-training.
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。
