SIGGRAPH 2020 论文列表

In this study, we propose a method to produce a simple 3D aerial image display with a wide viewing angle. In recent years, research on aerial imagery has been actively evolving. However, in order to create a 3D aerial image with a wide viewing angle, complex and large-scale devices are still required. In the present study, the visual field of a retro-transmissive optical system is widened by a symmetric mirror structure that also enables multiple 3D images to be seamlessly coupled midair to form a wide viewing angle 3D display. As a result, a viewing angle approximately three times that of the conventional system is realized using only simple optical systems.

Unlike playback of a video where a user can only interact by altering the video’s playback, a recorded Virtual Reality (VR) experience when played back in VR allows unprecedented avenues for interaction, including alteration of the content. Thus, we present Vuja De, the propensity of finding novelty in familiar experiences. It enables re-experiencing VR recordings anew each time because of the multiple interaction outcomes made possible. Subsequently, we discuss comprehensive interaction scenarios demonstrating the utility of interactive recordings in realizing diverse possibilities.

In this research, Visum, an immersive audio/visual application, which uses visual perception through an interface to create sound and visual objects is presented. Visual evoked potentials (VEP) are changes in brainwave electrical activity that are created when a visual stimulus is presented to an observer. Here, VEPs are generated through observing optical illusions. These VEPs are brought into an environment as three-dimensional sound and visual objects. The VEP objects can be played as an instrument via a user-controlled interface such as mixing and parameter control creating an evolving, dynamic, sounds and visuals. Visum is created for artistic and educational purposes.

In this paper, we propose a measurement and visualization system for spatial impulse responses that utilizes a moving handy microphone and Mixed Reality (MR). By enhancing the existing visualization system of sound intensity using MR, the proposed system aids in the visualization of spatial impulse responses using the estimation based on the signal measured by a moving microphone. As the visualization of the sound field varies with time, it is effective to understand the relationship between a complicated sound field, including the reflected sounds, and the reflecting objects in a room.

In this paper, we present a hybrid-haptic feedback system with a stationary fan and wireless controller with a fan. To explore the possibility of using our system to expand the users’ perception of environmental wind, we investigate the users’ perception of the wind blowing area combined with different distances and handheld wind speeds. The results show that compared with using the stationary fan only, the perceptional winds area is increased by at least 59.9% when using our hybrid-haptic device.

We present a novel method for fast and accurate ellipse detection based on an efficient arc grouping strategy. We first extract edges from the input image, and then obtain smooth arcs by recognizing sharp turns and inflexion points. To speed up ellipse generation, we group arcs by three intuitive yet more efficient rules, followed by a validation and a more distinctive cluster scheme to further improve the accuracy. Our approach achieves promising results on both synthetic and three real-world datasets.

There is lots of hidden information behind the sequential data and their sequences. We proposed a model for learning visual representation by solving order prediction task. We concatenated the frame pairs, instead of concatenating the feature pairs. This concatenation makes it possible to apply a 3D-CNN to extract features from the frame pairs. Also, we proposed a new grouping, which have achieved 80 percent accuracy on average. We have modified the shuffled video clips order prediction task to the shuffled frame order prediction, by selecting a frame from each clip, by random. Then this task was solved by applying our model.

We propose a projection for omnidirectional images called TubeMap which is efficient for omnidirectional view interpolation. This new projection keeps linearity on the captured scenes and covers almost every direction in a single region with no discontinuities by extending the projection planes of a cube map along the direction from one camera towards another. TubeMap makes it easy to construct correspondence between two omnidirectional images while eliminating distortion on interpolated views.

Establishing a robust measure for material similarity that correlates well with human perception is a long-standing problem. A recent work presented a deep learning model trained to produce a feature space that aligns with human perception by gathering human subjective measures. The resulting metric outperforms objective existing ones. In this work, we aim to understand whether this increased performance is a result of using human perceptual data or is due to the nature of feature learnt by deep learning models. We train similar networks with objective measures (BRDF similarity or classification task) and show that these networks can predict human judgements as well, suggesting that the non-linear features learnt by convolutional network might be a key to model material perception.

In the field of computational design of meta materials, complex patterns of meso-structures have seen increased interest because of the ability to locally control their flexibility through adjustment of the meso-structure parameters. Such structures come with a number of advantages like, despite the simplicity of their fabrication, their ability to nestle to sophisticated free-form surfaces. However, the simulation of such complex structures still comes with a high computational cost. We propose an approach to reduce this computational cost by abstracting the meso-structures and encoding the properties of their elastic deformation behavior into a different set of material parameters. We can thus obtain an approximation of the deformed pattern by simulating a simplified version of the pattern using the computed material parameters.

Passive motion is often used in physical skill transfer. This study investigated sense of agency during virtual drumming with visual, vibrotactile and auditory sensation feedback in three types of forearm motion: voluntary, passive and imagined. Agency judgement showed that visual, vibrotactile and auditory feedback contributed to sense of agency around 25 %- 45 % depending on the type of forearm motion. Motor command in voluntary motion contributed about 35 %, while proprioception in passive motion did about 25 %. Threse results suggested that multiple sensation feedback contributed to generate sense of agency.

Due to the limited range of depth sensors, the depth estimators for wild images were trained by ordinal data. Without the need of re-train, we proposed an approach to create seamlessly ordinal depth for 360 wild images. The 360 depth was applied to VR display for improving the virtual perception.

A powerful rigging pipeline is proposed to automatically rig the raw scans of human model with 1) fitting a Rigged Parametric Body Model (RPBM) to the scan with a novel and effective energy formulation, 2) cutting the scan to break self-occlusions automatically, 3) inpainting the geometry and texture in UV parametric space to create a complete avatar and 4) transferring the rigging information from the RPBM to the avatar. To further improve the fitting result, Semantic Deformation Component (SDC) is generated and utilized to replace the original shape blendshapes of the RPBM in the fitting stage, which can also be used for body reshaping. Our method is highly effective and robust in rigging the raw human scan models.

Crochet is a fabrication technique in which a 3D surface is created from yarn by interlacing loops formed with a special hook. Crochet patterns are typically represented using a standardized set of abstract pictorial symbols. Unfortunately, while this notation is enough for someone well-versed in the individual stitches, it does not directly show the yarn layout of stitches. This lack of specification makes it difficult for both novice users and computer programs to parse, visualize, and design crochet patterns. We demonstrate how to represent crochet patterns within the “stitch mesh” paradigm. That is, the pattern is represented using a library of tiles, where each tile contains yarn geometry, and tiles connect along their edges. In order to adapt stitch meshes to crochet, we introduce a special edge type which captures the idea of the current loop – the loop of yarn held on the crochet hook during fabrication. We also create a library of mesh face types which model commonly-used crochet stitches. We illustrate the richness of the crochet stitch faces by showing a number of examples including patterns generated from 3D models.