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The virtual reality (VR) and human-computer interaction (HCI) combination has radically changed the way users approach a virtual environment, increasing the feeling of VR immersion, and improving the user experience and usability. The evolution of these two technologies led to the focus on VR locomotion and interaction. Locomotion is generally controller-based, but today hand gesture recognition methods were also used for this purpose. However, hand gestures can be stressful for the user who has to keep the gesture activation for a long time to ensure locomotion, especially continuously. Likewise, in Head Mounted Display (HMD)-based virtual environment or Spherical-based system, the use of classic controllers for the 3D scene interaction could be unnatural for the user compared to using hand gestures such \eg pinching to grab 3D objects. To address these issues, we propose a user study comparing the use of the classic controllers (six-degree-of-freedom (6-DOF) or trackballs) in HMD and spherical-based systems, and the hand tracking and gestures in both VR immersive modes. In particular, we focused on the possible differences between spherical-based systems and HMD in terms of the level of immersion perceived by the user, the mode of user interaction (controller and hands), on the reaction of users concerning usefulness, easiness, and behavioral intention to use.

3D modeling of non-linear objects from stylized sketches is a challenge even for experts in Computer Graphics (CG). The extrapolation of objects parameters from a stylized sketch is a very complex and cumbersome task. In the present study, we propose a broker system that mediates between the modeler and the 3D modelling software and can transform a stylized sketch of a tree into a complete 3D model. The input sketches do not need to be accurate or detailed, and only need to represent a rudimentary outline of the tree that the modeler wishes to 3D-model. Our approach is based on a well-defined Deep Neural Network (DNN) architecture, we called TreeSketchNet (TSN), based on convolutions and able to generate Weber and Penn parameters that can be interpreted by the modelling software to generate a 3D model of a tree starting from a simple sketch. The training dataset consists of Synthetically-Generated \revision{(SG)} sketches that are associated with Weber-Penn parameters generated by a dedicated Blender modelling software add-on. The accuracy of the proposed method is demonstrated by testing the TSN with both synthetic and hand-made sketches. Finally, we provide a qualitative analysis of our results, by evaluating the coherence of the predicted parameters with several distinguishing features.

We present a method for turning a flash selfie taken with a smartphone into a photograph as if it was taken in a studio setting with uniform lighting. Our method uses a convolutional neural network trained on a set of pairs of photographs acquired in an ad-hoc acquisition campaign. Each pair consists of one photograph of a subject's face taken with the camera flash enabled and another one of the same subject in the same pose illuminated using a photographic studio-lighting setup. We show how our method can amend defects introduced by a close-up camera flash, such as specular highlights, shadows, skin shine, and flattened images.