A CNN-Based Framework for Enhancing 360° VR Experiences With Multisensorial Effects

Improving user experience during the delivery of immersive content is crucial for its success for both the content creators and audience. Creators can express themselves better with multisensory stimulation, while the audience can experience a higher level of involvement. The rapid development of mulsemedia devices provides better access for stimuli such as olfaction and haptics. Nevertheless, due to the required manual annotation process of adding mulsemedia effects, the amount of content available with sensorial effects is still limited. This work introduces an innovative mulsemedia-enhancement solution capable of automatically generating olfactory and haptic content based on 360° video content, with the use of neural networks. Two parallel neural networks are responsible for automatically adding scents to 360° videos: a scene detection network (responsible for static, global content) and an action detection network (responsible for dynamic, local content). A 360° video dataset with scent labels is also created and used for evaluating the robustness of the proposed solution. The solution achieves a 69.19% olfactory accuracy and 72.26% haptics accuracy during evaluation using two different datasets.