电池，摄像机，开拍！学习表达机器人电影摄影的语义控制空间
Batteries, camera, action! Learning a semantic control space for expressive robot cinematography
摘要：
飞行器正在彻底改变电影制片人通过组成新颖的空中和动态视点来捕捉演员镜头的方式。然而，尽管自主飞行技术取得了巨大进步，但生成富有表现力的相机行为仍然是一个挑战，需要非技术用户编辑大量不直观的控制参数。在这项工作中，我们开发了一个数据驱动的框架，使这些复杂的相机定位参数的语义空间（例如平静，愉快，建立）的编辑。首先，我们生成一个数据库的视频剪辑与不同范围的镜头在照片般逼真的模拟器，并使用数百名参与者在众包框架中获得分数的一组语义描述符为每个剪辑。接下来，我们分析描述符之间的相关性，并建立一个语义控制空间的基础上，电影摄影指导方针和人类感知研究。最后，我们学习了一个生成模型，可以将一组所需的语义视频描述符映射到低级别的摄像机轨迹参数。我们评估我们的系统，证明我们的模型成功地生成的镜头，由参与者评为具有预期程度的表达每个描述符。我们还表明，我们的模型推广到不同的场景在模拟和现实世界的实验。数据和视频可在https://sites.google.com/view/robotcam上找到。
英文摘要：
Aerial vehicles are revolutionizing the way film-makers can capture shots of actors by composing novel aerial and dynamic viewpoints. However, despite great advancements in autonomous flight technology, generating expressive camera behaviors is still a challenge and requires non-technical users to edit a large number of unintuitive control parameters. In this work, we develop a data-driven framework that enables editing of these complex camera positioning parameters in a semantic space (e.g. calm, enjoyable, establishing). First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip. Next, we analyze correlations between descriptors and build a semantic control space based on cinematography guidelines and human perception studies. Finally, we learn a generative model that can map a set of desired semantic video descriptors into low-level camera trajectory parameters. We evaluate our system by demonstrating that our model successfully generates shots that are rated by participants as having the expected degrees of expression for each descriptor. We also show that our models generalize to different scenes in both simulation and real-world experiments. Data and video found at: https://sites.google.com/view/robotcam.
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。
