In immersive applications, reconstructing full-body self-avatar motion remains a challenging task, particularly in the absence of lowerbody tracking devices. A promising approach involves supplementing sparse upper-body motion signals with 3D Cartesian joint positions estimated from external RGB cameras. While effective, such pose estimators are prone to inaccuracies, jitter, and occlusions, which can negatively impact reconstruction quality. In this work, we investigate the robustness of self-avatar animation models to such artifacts and to variations across pose estimators. We evaluate two state-of-the-art models, HMD-Poser and AvatarJLM, and show that integrating RGB-based pose data improves lower-body accuracy over VR-only baselines, even for inaccurate 3D pose. However, model performance degrades when training and testing rely on different pose estimators. This highlights the sensitivity of current approaches to estimator variability and underscores the need for estimator-aware training to ensure robustness in real-world deployment.

Stereo vision is essential for many applications. Currently, the synchronization of the streams coming from two cameras is done using mostly hardware. A software-based synchronization method would reduce the cost, weight and size of the entire system and allow for more flexibility when building such systems. With this goal in mind, we present here a comparison of different deep learning-based systems and prove that some are efficient and generalizable enough for such a task. This study paves the way to a production ready software-based video synchronization system.

This study explores the potential of enhancing interaction experiences, such as virtual reality (VR) games, through the use of computational attention models. Our proposed approach utilizes a saliency map generated by attention models to dynamically adjust game difficulty levels and to help in the game level design, resulting in a more immersive and engaging experience for users. To inform the development of this approach, we present an experimental setup that is able tp collect data in a VR environment and intends to be able to validate the adaptation of attention models to this domain. Through this work, we aim to create a framework for VR game design that leverages attention models to offer a new level of immersion and engagement for users. We believe our contributions have significant potential to enhance VR experiences and advance the field of game design.

Recently, various dimensionality reduction approaches have been proposed as alternatives to PCA or LDA. These improved approaches do not rely on a linearity assumption, and are hence capable of discovering more complex embeddings within different regions of the data sets. Despite their success on artificial datasets, it is not straightforward to predict which technique is the most appropriate for a given real dataset. In this paper, we empirically evaluate recent techniques on two real audio use cases: musical instrument loops used in music production and sound effects used in sound editing. ISOMAP and t-SNE are being compared to PCA in a visualization problem, where we end up with a two-dimensional view. Various evaluation measures are used: classification performance, as well as trustworthiness/continuity assessing the preservation of neighborhoods. Although PCA and ISOMAP can yield good continuity performance even locally (samples in the original space remain close-by in the low-dimensional one), they fail to preserve the structure of the data well enough to ensure that distinct subgroups remain separate in the visualization. We show that t-SNE presents the best performance, and can even be beneficial as a pre-processing stage for improving classification when the amount of labeled data is low.