Saliency-Free and Aesthetic-Aware Panoramic Video Navigation

Most of the existing panoramic video navigation approaches are saliency-driven, whereby off-the-shelf saliency detection tools are directly employed to aid the navigation approaches in localizing video content that should be incorporated into the navigation path. In view of the dilemma faced by our research community, we rethink if the "saliency clues" are really appropriate to serve the panoramic video navigation task. According to our in-depth investigation, we argue that using "saliency clues" cannot generate a satisfying navigation path, failing to well represent the given panoramic video, and the views in the navigation path are also low aesthetics. In this paper, we present a brand-new navigation paradigm. Although our model is still trained on eye-fixations, our methodology can additionally enable the trained model to perceive the "meaningful" degree of the given panoramic video content. Outwardly, the proposed new approach is saliency-free, but inwardly, it is developed from saliency but biasing more to be "meaningful-driven"; thus, it can generate a navigation path with more appropriate content coverage. Besides, this paper is the first attempt to devise an unsupervised learning scheme to ensure all localized meaningful views in the navigation path have high aesthetics. Thus, the navigation path generated by our approach can also bring users an enjoyable watching experience. As a new topic in its infancy, we have devised a series of quantitative evaluation schemes, including objective verifications and subjective user studies. All these innovative attempts would have great potential to inspire and promote this research field in the near future.