GAMES Webinar 2020 – 134期(绘制专题) | Lifan Wu(University of California, San Diego), Zexiang Xu(University of California, San Diego)
【GAMES Webinar 2020-134期】(绘制专题)
报告嘉宾1： (
报告时间：2020年4月9号早上10:30-11:15（北京时间）
报告题目：Accurate Appearance Preserving Prefiltering for Rendering Displacement-Mapped Surfaces
报告摘要：
Prefiltering the reflectance of a displacement-mapped surface while preserving its overall appearance is challenging, as smoothing a displacement map causes complex changes of illumination effects such as shadowing-masking and interreflection. In this paper, we introduce a new method that prefilters displacement maps and BRDFs jointly and constructs SVBRDFs at reduced resolutions. These SVBRDFs preserve the appearance of the input models by capturing both shadowing-masking and interreflection effects. To express our appearance-preserving SVBRDFs efficiently, we leverage a new representation that involves spatially varying NDFs and a novel scaling function that accurately captures micro-scale changes of shadowing, masking, and interreflection effects. Further, we show that the 6D scaling function can be factorized into a 2D function of surface location and a 4D function of direction. By exploiting the smoothness of these functions, we develop a simple and efficient factorization method that does not require computing the full scaling function. The resulting functions can be represented at low resolutions (e.g., 4^2 for the spatial function and 15^4 for the angular function), leading to minimal additional storage. Our method generalizes well to different types of geometries beyond Gaussian surfaces. Models prefiltered using our approach at different scales can be combined to form mipmaps, allowing accurate and anti-aliased level-of-detail (LoD) rendering.
讲者简介：
Lifan Wu is a fifth-year PhD student in Computer Science and Engineering Department at University of California, San Diego, advised by Prof. Ravi Ramamoorthi. He received his Bachelor’s degree in Computer Science from Tsinghua University in 2015. His research focuses on appearance modeling and physically-based rendering. He works on appearance prefiltering of micro-structure materials, which reduces the scene complexity and preserves its appearance to the maximum amount. He is also interested in applying sparse sampling and reconstruction techniques to Monte Carlo denoising and complex appearance simulation.
讲者个人主页: http://winmad.github.io/
报告嘉宾2： Zexiang Xu (University of California, San Diego)
报告时间：2020年4月9号早上11:15-12:00（北京时间）
报告题目：Learning Generative Models for Rendering Specular Microgeometry
报告摘要：
Rendering specular material appearance is a core problem of computer graphics. While smooth analytical material models are widely used, the high-frequency structure of real specular highlights requires considering discrete, finite microgeometry. Instead of explicit modeling and simulation of the surface microstructure (which was explored in previous work), we propose a novel direction: learning the high-frequency directional patterns from synthetic or measured examples, by training a generative adversarial network (GAN). A key challenge in applying GAN synthesis to spatially varying BRDFs is evaluating the reflectance for a single location and direction without the cost of evaluating the whole hemisphere. We resolve this using a novel method for partial evaluation of the generator network. We are also able to control large-scale spatial texture using a conditional GAN approach. The benefits of our approach include the ability to synthesize spatially large results without repetition, support for learning from measured data, and evaluation performance independent of the complexity of the dataset synthesis or measurement.
讲者简介：
Zexiang Xu is currently a fifth-year Ph.D. candidate in computer science at University of California, San Diego. His advisor is Prof. Ravi Ramamoorthi. His research interests lie at the intersection of computer graphics and computer vision, including relighting, view synthesis, appearance acquisition, and 3D reconstruction.
讲者个人主页: http://cseweb.ucsd.edu/~zex014/
主持人简介：
讲者个人主页：
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