GAMES Webinar 2023 – 266期(高效材质处理) | 樊家辉(南京理工大学),靳文华(南京理工大学)

【GAMES Webinar 2023-266期】(绘制专题-高效材质处理)



报告题目:Neural Layered BRDFs


Bidirectional reflectance distribution functions (BRDFs) are pervasively used in computer graphics to produce realistic physically-based appearance. Many common materials in the real world have more than one layer, like wood, skin, car paint, and many decorative materials. However, precise simulation of layered material optics is non-trivial. The most accurate approaches rely on Monte Carlo random walks to simulate the light transport within the layers, leading to high variance and cost. Other approaches are efficient, but less accurate. In this paper, we propose to perform layering in the neural space, by compressing BRDFs into latent codes via a proposed representation neural network, and performing a learned layering operation on these latent vectors via a layering network. Our BRDF evaluation is noise-free and computationally efficient, compared to the state-of-the-art approach; it is also a first step towards a “neural algebra” of operations on BRDFs in a latent space.


樊家辉,男,1998年生。现于南京理工大学计算机科学与工程学院攻读博士学位,导师为杨健教授和王贝贝教授。研究方向为真实感渲染中的材质表达与神经网络,具体内容包含复杂材质的神经网络表达、微表面模型理论和闪亮微结构的高效渲染。相关研究工作发表在SIGGRAPH、IEEE TVCG等国际顶级会议、期刊上。曾获2022年GAMES最佳海报奖、2022年Style3D图形学奖学金等荣誉奖项。曾任期刊Machine Intelligence Research的审稿工作。




报告题目:Woven Fabric Capture from a Single Photo


Digitally reproducing the appearance of woven fabrics is important in many applications of realistic rendering, from interior scenes to virtual characters. However, designing realistic shading models and capturing real fabric samples are both challenging tasks. Previous work ranges from applying generic shading models not meant for fabrics, to data-driven approaches scanning fabrics requiring expensive setups and large data. In this paper, we propose a woven fabric material model and a parameter estimation approach for it. Our lightweight forward shading model treats yarns as bent and twisted cylinders, shading these using a microflake-based bidirectional reflectance distribution function (BRDF) model. We propose a simple fabric capture configuration, wrapping the fabric sample on a cylinder of known radius and capturing a single image under known camera and light positions. Our inverse rendering pipeline consists of a neural network to estimate initial fabric parameters and an optimization based on differentiable rendering to refine the results. Our fabric parameter estimation achieves high-quality recovery of measured woven fabric samples, which can be used for efficient rendering and further edited.




过洁,现任南京大学计算机科学与技术系、计算机软件新技术国家重点实验室副研究员,南京大学-OPPO软件技术创新联合实验室副主任,江苏省计算机学会图形图像专委会和江苏省工程师学会虚拟现实与元宇宙专委会秘书长。主要研究方向为计算机图形学、虚拟现实和三维视觉,在国内外主流期刊和会议上发表高水平论文70余篇,包括SIGGRAPH、IEEE TVCG、CVPR、ICCV、ECCV、IEEE VR等。个人主页:。


GAMES主页的“使用教程”中有 “如何观看GAMES Webinar直播?”及“如何加入GAMES微信群?”的信息;

You may also like...