# Pano2Vid: Automatic Cinematography for Watching 360circ Videos

arXiv: https://arxiv.org/abs/1612.02335 · HTML: https://mlsanity.com/paper/1612.02335/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2016.

## Built on by (most cited first, 22 of 22)

- [Pano-AVQA: Grounded Audio-Visual Question Answering on 360^circ Videos](https://mlsanity.com/paper/2110.05122/) (2021)
- [OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion](https://mlsanity.com/paper/2203.00838/) (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)
- [Panoramic Vision Transformer for Saliency Detection in 360deg Videos](https://mlsanity.com/paper/2209.08956/) (2022)
- [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)
- [OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas](https://mlsanity.com/paper/2202.08752/) (2022)
- [Spherical World-Locking for Audio-Visual Localization in Egocentric Videos](https://mlsanity.com/paper/2408.05364/) (2024)
- [Camera View Adjustment Prediction for Improving Image Composition](https://mlsanity.com/paper/2104.07608/) (2021)
- [ASOD60K: An Audio-Induced Salient Object Detection Dataset for Panoramic Videos](https://mlsanity.com/paper/2107.11629/) (2021)
- [An Integrated System for Spatio-Temporal Summarization of 360-degrees Videos](https://mlsanity.com/paper/2312.02576/) (2023)
- [Towards a Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents](https://mlsanity.com/paper/2104.06780/) (2021)
- [WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning](https://mlsanity.com/paper/2305.13901/) (2023)
- [Switch-a-View: View Selection Learned from Unlabeled In-the-wild Videos](https://mlsanity.com/paper/2412.18386/) (2024)
- [Automatic Camera Control and Directing with an Ultra-High-Definition Collaborative Recording System](https://mlsanity.com/paper/2208.05213/) (2022)
- [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)
- [EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues](https://mlsanity.com/paper/2502.02172/) (2025)
- [TSalV360: A Method and Dataset for Text-driven Saliency Detection in 360-Degrees Videos](https://mlsanity.com/paper/2509.26208/) (2025)
- [Sphere-Depth: A Benchmark for Depth Estimation Methods with Varying Spherical Camera Orientations](https://mlsanity.com/paper/2604.23432/) (2026)

## Abstract (arXiv)

We introduce the novel task of Pano2Vid - automatic cinematography in panoramic 360circ videos. Given a 360circ video, the goal is to direct an imaginary camera to virtually capture natural-looking normal field-of-view (NFOV) video. By selecting "where to look" within the panorama at each time step, Pano2Vid aims to free both the videographer and the end viewer from the task of determining what to watch. Towards this goal, we first compile a dataset of 360circ videos downloaded from the web, together with human-edited NFOV camera trajectories to facilitate evaluation. Next, we propose AutoCam, a data-driven approach to solve the Pano2Vid task. AutoCam leverages NFOV web video to discriminatively identify space-time "glimpses" of interest at each time instant, and then uses dynamic programming to select optimal human-like camera trajectories. Through experimental evaluation on multiple newly defined Pano2Vid performance measures against several baselines, we show that our method successfully produces informative videos that could conceivably have been captured by human videographers.