# Making 360circ Video Watchable in 2D: Learning Videography for Click Free Viewing

arXiv: https://arxiv.org/abs/1703.00495 · HTML: https://mlsanity.com/paper/1703.00495/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 13 of 13)

- [Waymo Open Dataset: Panoramic Video Panoptic Segmentation](https://mlsanity.com/paper/2206.07704/) (2022)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [ScanGAN360: A Generative Model of Realistic Scanpaths for 360circ Images](https://mlsanity.com/paper/2103.13922/) (2021)
- [Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence with Egocentric-Exocentric Vision](https://mlsanity.com/paper/2506.06253/) (2025)
- [Video Summarization Overview](https://mlsanity.com/paper/2210.11707/) (2022)
- [Which Viewpoint Shows it Best? Language for Weakly Supervising View Selection in Multi-view Instructional Videos](https://mlsanity.com/paper/2411.08753/) (2024)
- [Field-of-View IoU for Object Detection in 360deg Images](https://mlsanity.com/paper/2202.03176/) (2022)
- [An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos](https://mlsanity.com/paper/2312.02576/) (2023)
- [WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning](https://mlsanity.com/paper/2305.13901/) (2023)
- [Saliency-based Multiple Region of Interest Detection from a Single 360deg image](https://mlsanity.com/paper/2209.03656/) (2022)
- [FindView: Precise Target View Localization Task for Look Around Agents](https://mlsanity.com/paper/2303.09054/) (2023)
- [A Human-Annotated Video Dataset for Training and Evaluation of 360-Degree Video Summarization Methods](https://mlsanity.com/paper/2406.02991/) (2024)
- [TSalV360: A Method and Dataset for Text-driven Saliency Detection in 360-Degrees Videos](https://mlsanity.com/paper/2509.26208/) (2025)

## Abstract (arXiv)

360circ video requires human viewers to actively control "where" to look while watching the video. Although it provides a more immersive experience of the visual content, it also introduces additional burden for viewers; awkward interfaces to navigate the video lead to suboptimal viewing experiences. Virtual cinematography is an appealing direction to remedy these problems, but conventional methods are limited to virtual environments or rely on hand-crafted heuristics. We propose a new algorithm for virtual cinematography that automatically controls a virtual camera within a 360circ video. Compared to the state of the art, our algorithm allows more general camera control, avoids redundant outputs, and extracts its output videos substantially more efficiently. Experimental results on over 7 hours of real "in the wild" video show that our generalized camera control is crucial for viewing 360circ video, while the proposed efficient algorithm is essential for making the generalized control computationally tractable.