Abstract We present an approach for multi-agent navigation that facilitates face-to-face interaction in virtual crowds, based on a novel interaction velocity prediction (IVP) algorithm. Our user evaluation indicates that such techniques enabling face-to-face interactions can improve the sense of presence felt by the user. The virtual agents using these algorithms also appear more responsive and are able to elicit more reaction from the users. Paper F2FCrowds: Planning Agent Movements to Enable Face-to-Face Interactions, PRESENCE 2017.
Abstract We present an approach for multi-agent navigation that facilitates face-to-face interaction in virtual crowds, based on a novel interaction velocity prediction (IVP) algorithm. Our user evaluation indicates that such techniques enabling face-to-face interactions can improve the sense of presence felt by the user. The virtual agents using these algorithms also appear more responsive and are able to elicit more reaction from the users. Paper F2FCrowds: Planning Agent Movements to Enable Face-to-Face Interactions, PRESENCE 2017.
FAR: Fourier Object Disentanglement (FO) empowers the network to intrinsically separate out the moving human agent from the background, without the need for any annotated object detection bounding boxes. This enables our network to explicitly focus on the low resolution human agent performing action, and not just learn from background cues. Space-Time Fourier Attention (FA) elegantly exploits the mathematical properties of the Fourier transform to imbibe the properties of self-attention and capture contextual knowledge and long-range space-time dependencies at a much lower computational complexity.
Abstract We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RIR takes rectangular room dimensions, listener and speaker positions, and reverberation time as inputs and generates specular and diffuse reflections for a given acoustic environment. Our FAST-RIR is capable of generating RIRs for a given input reverberation time with an average error of 0.02s. We evaluate our generated RIRs in automatic speech recognition (ASR) applications using Google Speech API, Microsoft Speech API, and Kaldi tools.
Abstract We present a new approach to improve the friendliness and warmth of a virtual agent in an AR environment by generating appropriate movement characteristics. Our algorithm is based on a novel data-driven friendliness model that is computed using a user-study and psychological characteristics. We investigated the perception of a user in an AR setting and observed that an FVA has a statistically significant improvement in the perceived friendliness and social presence of a user compared to an agent without the friendliness modeling.
Abstract We present a new approach to improve the friendliness and warmth of a virtual agent in an AR environment by generating appropriate movement characteristics. Our algorithm is based on a novel data-driven friendliness model that is computed using a user-study and psychological characteristics. We investigated the perception of a user in an AR setting and observed that an FVA has a statistically significant improvement in the perceived friendliness and social presence of a user compared to an agent without the friendliness modeling.
Abstract Fabric materials are central to recreating realistic appearance of avatars in a virtual world and many VR applications, ranging from virtual try-on, teleconferencing, to character animation. We propose an end-to-end network model that uses video input to estimate the fabric materials of the garment worn by a human or an avatar in a virtual world. To achieve the high accuracy, we jointly learn human body and the garment geometry as conditions to material prediction.
Network Architecture: We show the trajectory and behavior prediction for the ith road-agent (red circle in the traffic-graphs). The input consists of the spatial coordinates over the past T seconds as well as the eigenvectors (green rectangles, each shade of green represents the index of the eigenvectors) of the traffic-graphs corresponding to the first T traffic-graphs. We perform spectral clustering on the predicted eigenvectors from the second stream (orange block) to regularize the original loss function and perform backpropagation on the new loss function to improve long-term prediction.
Abstract We present Frozone, a novel algorithm to deal with the Freezing Robot Problem (FRP) that arises when a robot navigates through dense scenarios and crowds. Our method senses and explicitly predicts the trajectories of pedestrians and constructs a Potential Freezing Zone (PFZ); a spatial zone where the robot could freeze or be obtrusive to humans. Our formulation computes a deviation velocity to avoid the PFZ, which also accounts for social constraints.
Abstract The generation of realistic apparel model has become increasingly popular as a result of the rapid pace of change in fashion trends and the growing need for garment models in various applications such as virtual try-on. For such application requirements, it is important to have a general cloth model that can represent a diverse set of garments. Previous studies often make certain assumptions about the garment, such as the topology or suited body shape.