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Watching a 360{\deg} sports video requires a viewer to continuously select aviewing angle, either through a sequence of mouse clicks or head movements. Torelieve the viewer from this "360 piloting" task, we propose "deep 360 pilot"-- a deep learning-based agent for piloting through 360{\deg} sports videosautomatically. At each frame, the agent observes a panoramic image and has theknowledge of previously selected viewing angles. The task of the agent is toshift the current viewing angle (i.e. action) to the next preferred one (i.e.,goal). We propose to directly learn an online policy of the agent from data. Weuse the policy gradient technique to jointly train our pipeline: by minimizing(1) a regression loss measuring the distance between the selected and groundtruth viewing angles, (2) a smoothness loss encouraging smooth transition inviewing angle, and (3) maximizing an expected reward of focusing on aforeground object. To evaluate our method, we build a new 360-Sports videodataset consisting of five sports domains. We train domain-specific agents andachieve the best performance on viewing angle selection accuracy and transitionsmoothness compared to [51] and other baselines.

Deep 360 Pilot: Learning a Deep Agent for Piloting through 360{\deg} Sports Video