40,219 research outputs found

    A Dose of Reality: Overcoming Usability Challenges in VR Head-Mounted Displays

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    We identify usability challenges facing consumers adopting Virtual Reality (VR) head-mounted displays (HMDs) in a survey of 108 VR HMD users. Users reported significant issues in interacting with, and being aware of their real-world context when using a HMD. Building upon existing work on blending real and virtual environments, we performed three design studies to address these usability concerns. In a typing study, we show that augmenting VR with a view of reality significantly corrected the performance impairment of typing in VR. We then investigated how much reality should be incorporated and when, so as to preserve users’ sense of presence in VR. For interaction with objects and peripherals, we found that selectively presenting reality as users engaged with it was optimal in terms of performance and users’ sense of presence. Finally, we investigated how this selective, engagement-dependent approach could be applied in social environments, to support the user’s awareness of the proximity and presence of others

    Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture

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    This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dissolve, and animal hybridization.Comment: Accepted to CVPR'1

    Space-Time Transfinite Interpolation of Volumetric Material Properties

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    The paper presents a novel technique based on extension of a general mathematical method of transfinite interpolation to solve an actual problem in the context of a heterogeneous volume modelling area. It deals with time-dependent changes to the volumetric material properties (material density, colour and others) as a transformation of the volumetric material distributions in space-time accompanying geometric shape transformations such as metamorphosis. The main idea is to represent the geometry of both objects by scalar fields with distance properties, to establish in a higher-dimensional space a time gap during which the geometric transformation takes place, and to use these scalar fields to apply the new space-time transfinite interpolation to volumetric material attributes within this time gap. The proposed solution is analytical in its nature, does not require heavy numerical computations and can be used in real-time applications. Applications of this technique also include texturing and displacement mapping of time-variant surfaces, and parametric design of volumetric microstructures

    Semantic Photo Manipulation with a Generative Image Prior

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    Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existing natural photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method.Comment: SIGGRAPH 201
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