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Espensen Morris posted an update 6 months, 1 week ago
Redirected Walking (RDW) steering algorithms have traditionally relied on human-engineered logic. However, recent advances in reinforcement learning (RL) have produced systems that surpass human performance on a variety of control tasks. This paper investigates the potential of using RL to develop a novel reactive steering algorithm for RDW. Our approach uses RL to train a deep neural network that directly prescribes the rotation, translation, and curvature gains to transform a virtual environment given a user’s position and orientation in the tracked space. We compare our learned algorithm to steer-to-center using simulated and real paths. We found that our algorithm outperforms steer-to-center on simulated paths, and found no significant difference on distance traveled on real paths. We demonstrate that when modeled as a continuous control problem, RDW is a suitable domain for RL, and moving forward, our general framework provides a promising path towards an optimal RDW steering algorithm.We propose and evaluate novel pseudo-haptic techniques to display mass and mass distribution for proxy-based object manipulation in virtual reality. These techniques are specifically designed to generate haptic effects during the object’s rotation. find more They rely on manipulating the mapping between visual cues of motion and kinesthetic cues of force to generate a sense of heaviness, which alters the perception of the object’s mass-related properties without changing the physical proxy. First we present a technique to display an object’s mass by scaling its rotational motion relative to its mass. A psycho-physical experiment demonstrates that this technique effectively generates correct perceptions of relative mass between two virtual objects. We then present two pseudo-haptic techniques designed to display an object’s mass distribution. One of them relies on manipulating the pivot point of rotation, while the other adjusts rotational motion based on the real-time dynamics of the moving object. An empirical study shows that both techniques can influence perception of mass distribution, with the second technique being significantly more effective.Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acuity of the human visual system, along with a resulting need to follow the gaze of the eye as it moves, a process referred to as foveation. A display that moves its content along with the eye may be called a Foveated Display, though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity. We therefore recommend a definition for the term Foveated Display that accepts both of these interpretations. Furthermore, we include a simplified model for human visual Acuity Distribution Functions (ADFs) at various levels of visual acuity, across wide fields of view and propose comparison of this ADF with the Resolution Distribution Function of a foveated display for evaluation of its resolution at a particular gaze direction. We also provide a taxonomy to allow the field to meaningfully compare and contrast various aspects of foveated displays in a display and optical technology-agnostic manner.The traditional salient object detection models can be divided into several classes based on the low-level features of images and contrast between the pixels. This paper proposes an adversarial learning model (ALM) that includes the generative model and discriminative model. The ALM uses the original image as an input of the generative model to extract the high-level features and forms an initial salient map. Then, the discriminative model is utilized to compare differences in the features between the initial salient map and the ground truth, and the obtained differences are sent to the convolutional layers of the generative model to adjust the parameters for the generative model updating. Due to the serial-iterative adjustment, the salient map of the generative model becomes more similar to the ground truth. Lastly, the ALM forms the salient map fused with the super-pixels by enhancing the color and texture features, so the final salient map is obtained. The ALM is not limited to the color and texture features; on the contrary, it fuses multiple features and achieves good results in the salient target extraction. The experimental results show that ALM performs better than the other ten state-of-the-art models on three different datasets. Thus, the proposed ALM is widely applicable to the salient target extraction.Temporal cues embedded in videos provide important clues for person Re-Identification (ReID). To efficiently exploit temporal cues with a compact neural network, this work proposes a novel 3D convolution layer called Multi-scale 3D (M3D) convolution layer. The M3D layer is easy to implement and could be inserted into traditional 2D convolution networks to learn multi-scale temporal cues by end-to-end training. According to its inserted location, the M3D layer has two variants, i.e., local M3D layer and global M3D layer, respectively. The local M3D layer is inserted between 2D convolution layers to learn spatial-temporal cues among adjacent 2D feature maps. The global M3D layer is computed on adjacent frame feature vectors to learn their global temporal relations. The local and global M3D layers hence learn complementary temporal cues. Their combination introduces a fraction of parameters to traditional 2D CNN, but leads to the strong multi-scale temporal feature learning capability. The learned temporal feature is fused with a spatial feature to compose the final spatial-temporal representation for video person ReID. Evaluations on four widely used video person ReID datasets, i.e., MARS, DukeMTMC-VideoReID, PRID2011, and iLIDS-VID demonstrate the substantial advantages of our method over the state-of-the art. For example, it achieves rank1 accuracy of 88.63% on MARS without re-ranking. Our method also achieves a reasonable trade-off between ReID accuracy and model size, e.g., it saves about 40% parameters of I3D CNN.
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