This work presents CineTransfer, an algorithmic framework that drives a robot to record a video sequence that mimics the cinematographic style of an input video. We propose features that abstract the aesthetic style of the input video, so the robot can transfer this style to a scene with visual details that are significantly different from the input video. The framework builds upon CineMPC, a tool that allows users to control cinematographic features, like subjects' position on the image and the depth of field, by manipulating the intrinsics and extrinsics of a cinematographic camera. However, CineMPC requires a human expert to specify the desired style of the shot (composition, camera motion, zoom, focus, etc). CineTransfer bridges this gap, aiming a fully autonomous cinematographic platform. The user chooses a single input video as a style guide. CineTransfer extracts and optimizes two important style features, the composition of the subject in the image and the scene depth of field, and provides instructions for CineMPC to control the robot to record an output sequence that matches these features as closely as possible. In contrast with other style transfer methods, our approach is a lightweight and portable framework which does not require deep network training or extensive datasets. Experiments with real and simulated videos demonstrate the system's ability to analyze and transfer style between recordings, and are available in the supplementary video.

翻译：本文提出CineTransfer算法框架，该框架可驱动机器人录制视频序列，使其模拟输入视频的电影风格。我们提出能够抽象输入视频美学风格的特征，使机器人能够将该风格迁移至视觉细节与输入视频显著不同的场景。该框架基于CineMPC工具构建，后者允许用户通过操控电影摄像机的内参和外参控制电影特征（如被摄主体在图像中的位置与景深）。然而，CineMPC需要人类专家指定期望的镜头风格（构图、摄像机运动、变焦、对焦等）。CineTransfer填补了这一空白，旨在实现全自主电影摄影平台。用户只需选择单个输入视频作为风格参照，CineTransfer即可提取并优化两种重要风格特征——被摄主体在图像中的构图与场景景深，进而向CineMPC提供指令，驱动机器人录制最接近这些特征的输出序列。与其他风格迁移方法相比，本方法作为轻量级可移植框架，无需深度网络训练或大规模数据集。基于真实与模拟视频的实验表明，系统具备分析并迁移录制品风格的能力，相关实验视频见补充材料。