SIGGRAPH 2020 论文列表

Filipe Antonio de Barros Reis Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas (UNICAMP) Campinas, Brazil Paula Dornhofer Paro Costa Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas (UNICAMP) Campinas, Brazil José Mario de Martino Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas (UNICAMP) Campinas, Brazil

We present ”CrowbarLimbs”, a new method with two deformable extending virtual limbs for text entry in virtual reality (VR) which relies on a crowbar-like metaphor. Text entry is the basis of many applications but remains challenging in VR environments, where some body parts of a user may quickly get fatigued by using previous selection-based methods [Speicher et al. 2018], such as the ray metaphor [Lee and Kim 2017] and ”DrumStick” [Doronichev 2016]. By adding two deformable virtual limbs and placing the virtual keyboard at a user-preferred location, our method can help users to place their hands in a comfortable posture, thus reducing the physical fatigue of different body parts, such as arms and shoulders. To the best knowledge of ours, previous text entry papers have not yet discussed which metaphor is more suitable for reducing physical fatigue. We thus introduce “CrowbarLimbs” to allow less fatigue, good system usability, and comparable text entry speed/accuracy to previous methods.

We present a new spatially-varying dynamic range compression algorithm for high dynamic range (HDR) images based on bound-constrained optimization using soft constraints. Rather than explicitly attenuating gradients as in previous work, we minimize an objective function to instead compute a globally optimal manipulation of input pixel differences. Our framework provides simple yet effective preservation of visually important image properties, such as order statistics and global consistency, that requires little to no parameter tuning. Our results are free of haloing, washout, and other artifacts, while retaining detail across the image’s full range. The speed of our algorithm and flexibility of the constraint framework allows our method to be easily extended to video.

We present a novel method for organizing Bézier bounded geometry based on affine similarity and visual saliency. For this computationally expensive (many-to-many) problem, we propose a highly parallel algorithm, leveraging programmable GPU pipeline, computing pairwise affine transformations to classify Bézier geometry into clusters, where paths in each cluster are affine transforms of each other. Using these clusters, we propose a method to organize paths into meaningful groups, even from complex, unstructured geometry, common in real-world vector art. We propose a function to quantify relative importance of these groups, using attributes such as complexity and frequency of occurrence, resulting in meaningful, reusable assets. Our method is both robust and performant, capable of processing thousands of paths within milliseconds.

One of the problems in virtual reality is a natural interaction with a virtual object. In this study, we focus on an interaction with a virtual object outside a user’s field of view. The position of the virtual object is estimated without looking at it by using frequency change and/or sound pressure change as auditory stimuli.

In this paper, we present a simple and intuitive approach to transfer existing FACS (Facial Action Coding System) based facial rig and its data set on to a different facial model that has a different mesh topology to support rigging and animation pipeline for visual effects films, 3D animations, and video games. Facial rigs are custom built for each character and are time consuming and complex to create, and even minor tweaks on the model typology may cause the rigging process to start from scratch. In order to maximize the data reuse, this system can apply one set of data onto a variety of human or creature facial rigs.

Figure 1: The model of Belcour [Belcour 2018] suffers from severe energy losses with scattering volumes due to its singlescattering approximation and its lack of support for back-scattering. Our transfer matrix approach overcomes these issues and provides results close to the ground-truth computed with Guo’s stochastic approach [Guo et al. 2018]. In these examples, a unit depth non-absorbing medium with scattering coefficient σs = 0.755 and varying Henyey-Greenstein (HG) д parameters lies on top of a smooth gold substrate. HG д asymmetry parameter is respectively set to 0.9 and to 0 in the left and right inset.

In this work, we present a method to turn still life paintings with global illumination effects into dynamic paintings with moving lights. Our method specifically focuses on still life images containing (1) glass objects, which can have specular highlights with Fresnel effect and (2) fruits, which can have reflection and subsurface scattering. Our goal is to preserve the original look-and-feel of the still-life paintings while allowing the user to move the light source anywhere in the scene, causing the shadows, diffuse shading, as well as reflections and specular highlights to move according to the new light position. We have provided a proof of concept based on an original digital painting. This method can be used to turn any similar still life painting into a dynamic painting.

(a) Viewing from the user , the hand is in front of the mid-air 3DCG object, so the hand is occluding the 3DCG object. (b) Viewing from the user, part of the hand is behind the mid-air 3DCG object. Occluding the hand with the mid-air 3DCG object realized by projecting an appropriate image to the hand automatically. (c) Another example of occluding the hand with the mid-air 3DCG object based on projecting an image.

Although artists’ actions in photo retouching appear to be highly nonlinear in nature and very difficult to characterize analytically, we find that the net effects of interactively editing a mundane image to a desired appearance can be modeled, in most cases, by a parametric monotonically non-decreasing global tone mapping function in the luminance axis and by a global affine transform in the chrominance plane. This allows us to greatly simplify the existing CNN methods for mimicking the artists in photo retouching, and design a new artful image regeneration network (AIRNet). The objective of AIRNet is to learn the image-dependent parameters of the luminance tone mapping function and the affine chrominance transform, rather than learning the end-to-end pixel level mapping as in the standard practice of current CNN methods for image restoration and enhancement. The proposed new approach reduces the complexity of the neural network by two orders of magnitude, and as a side benefit, it also improves the robustness and the generation capability at the inference stage.

This poster describes the implementation of a performant 2D drawing application in the browser that renders Signed Distance Functions (SDF) compiled from user input. SDFs are well suited for CAD applications because they reveal elegant boolean operations and efficiently allow for superior anti-aliasing. Because of their reliance on the GPU, SDFs have not traditionally lent themselves to graphical user input. By compiling shaders on the fly from user input we are able to seamlessly interact with rendered SDFs in a CAD interface.

In this poster, we present a heterogeneous architecture for estimating 6D object pose from RGB images. First, we use a two-stream network to extract robust 3D-to-2D embedding feature correspondence. The segmentation stream processes the RGB information and spatial features individually. Then, we construct another fusion network to couple color and positional features, and predict the locations of keypoints in the regression stream. The pose can be obtained by an efficient RANSAC-based PnP algorithm. Moreover, we design an end-to-end iterative pose refinement procedure that further improves the reliable pose estimation. Our method outperforms state-of-the-art approaches in two public datasets.