Quentin Guimard

Prediction of head movements in immersive media is key to designing efficient streaming systems able to focus the bandwidth budget on visible areas of the content. However, most of the numerous proposals made to predict user head motion in 360° images and videos do not explicitly consider a prominent characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. To our knowledge, this is the first work that considers the problem of multiple head motion prediction for 360° video streaming. We introduce our discrete variational multiple sequence (DVMS) learning framework, which builds on deep latent variable models. We design a training procedure to obtain a flexible, lightweight stochastic prediction model compatible with sequence-to-sequence neural architectures. Experimental results on four different datasets show that DVMS outperforms competitors adapted from the self-driving domain by up to 41% on prediction horizons up to 5 s, at lower computational and memory costs. To understand how the learned features account for the motion uncertainty, we analyze the structure of the learned latent space and connect it with the physical properties of the trajectories. We also introduce a method to estimate the likelihood of each generated trajectory, enabling the integration of DVMS in a streaming system. We hence deploy an extensive evaluation of the interest of our DVMS proposal for a streaming system. To do so, we first introduce a new Python-based 360° streaming simulator that we make available to the community. On real-world user, video, and networking data, we show that predicting multiple trajectories yields higher fairness between the traces, the gains for 20–30% of the users reaching up to 10% in visual quality for the best number K of trajectories to generate.

Prediction of head movements in immersive media is key to design efficient streaming systems able to focus the bandwidth budget on visible areas of the content. Numerous proposals have therefore been made in the recent years to predict 360° images and videos. However, the performance of these models is limited by a main characteristic of the head motion data: its intrinsic uncertainty. In this article, we present an approach to generate multiple plausible futures of head motion in 360° videos, given a common past trajectory. Our method provides likelihood estimates of every predicted trajectory, enabling direct integration in streaming optimization. To the best of our knowledge, this is the first work that considers the problem of multiple head motion prediction for 360° video streaming. We first quantify this uncertainty from the data. We then introduce our discrete variational multiple sequence (DVMS) learning framework, which builds on deep latent variable models. We design a training procedure to obtain a flexible and lightweight stochastic prediction model compatible with sequence-to-sequence recurrent neural architectures. Experimental results on 3 different datasets show that our method DVMS outperforms competitors adapted from the self-driving domain by up to 37% on prediction horizons up to 5 sec., at lower computational and memory costs. Finally, we design a method to estimate the respective likelihoods of the multiple predicted trajectories, by exploiting the stationarity of the distribution of the prediction error over the latent space. Experimental results on 3 datasets show the quality of these estimates, and how they depend on the video category.

While immersive media have been shown to generate more intense emotions, saliency information has been shown to be a key component for the assessment of their quality, owing to the various portions of the sphere (viewports) a user can attend. In this article, we investigate the tri-partite connection between user attention, user emotion and visual content in immersive environments. To do so, we present a new dataset enabling the analysis of different types of saliency, both low-level and high-level, in connection with the user’s state in 360 ◦ videos. Head and gaze movements are recorded along with self-reports and continuous physiological measurements of emotions. We then study how the accuracy of saliency estimators in predicting user attention depends on user-reported and physiologically-sensed emotional perceptions. Our results show that high-level saliency better predicts user attention for higher levels of arousal. We discuss how this work serves as a first step to understand and predict user attention and intents in immersive interactive environments.

While 360° videos watched in a VR headset are gaining in popularity, it is necessary to lower the required bandwidth to stream these immersive videos and obtain a satisfying quality of experience. Doing so requires predicting the user’s head motion in advance, which has been tackled by a number of recent prediction methods considering the video content and the user’s past motion. However, human motion is a complex process that can depend on many more parameters, including the type of attentional phase the user is currently in, and their emotions, which can be difficult to capture. This is the first article to investigate the effects of user emotions on the predictability of head motion, in connection with video-centric parameters. We formulate and verify hypotheses, and construct a structural equation model of emotion, motion and predictability. We show that the prediction error is higher for higher valence ratings, and that this relationship is mediated by head speed. We also show that the prediction error is lower for higher arousal, but that spatial information moderates the effect of arousal on predictability. This work opens the path to better capture important factors in human motion, to help improve the training process of head motion predictors.