Generative AI has taken the graphics world by a storm. New models, redefining the industry, seem to emerge almost every other day. But the fusions of art and technology are not new. Every year ACM SIGGRAPH (Special Interest Group on Computer Graphics and Interactive Techniques) presents an event where pixels and algorithms paint dreams. The distinguished academic organisation Association of Computing Machinery put up a show in Los Angeles from August 6 to 10.
Among the notable speakers, NVIDIA CEO Huang Jensen took the stage this year to announce several new products and research focusing on generative AI, computer graphics, and the company’s role in OpenUSD developments.
Read more: After GTC, NVIDIA Rides the Generative AI Wave at SIGGRAPH
This year marked the 50th anniversary of SIGGRAPH. The technical paper awards were newly introduced in 2022 since these papers serve as the pillar for scholarly work. Technical Papers Chair Alla Sheffer highlighted these award-winning papers and thanked the selection committee who chose the Best Papers out of the pool of hundreds.
Here are the 5 best award-winning papers from this year’s conference:
Split-Lohmann Multifocal Displays
The new paper introduces an impressive near-eye 3D display that quickly creates virtual worlds and lets users focus naturally on objects at different distances. This means you can enjoy realistic 3D videos and games like never before, feeling fully immersed.
Authors: Yingsi Qin, Wei-Yu Chen, Matthew O’Toole, Aswin C. Sankaranarayanan, Carnegie Mellon University
Differentiable Stripe Patterns for Inverse Design of Structured Surfaces
In this work, researchers presented an innovative technique called “Differentiable Stripe Patterns.” This computational method automates the design of physical surfaces with distinct stripe-shaped, dual-material arrangements.
The team has developed a tool that employs optimization through gradients to automatically generate stripe patterns. These patterns are tailored to closely match specific goals for overall mechanical performance.
Authors: Juan Sebastian Montes Maestre, Yinwei Du, Ronan Hinchet, Stelian Coros, Bernhard Thomaszewski, ETH Zürich
Globally Consistent Normal Orientation for Point Clouds by Regularizing the Winding-number Field
The researchers have put forth a smooth objective function to define the criteria for an acceptable winding-number field. This innovation allows the determination of globally consistent normal orientations, even when starting from an initial set of entirely random normals.
Authors: Rui Xu, Shandong University; Zhiyang Dou, The University of Hong Kong; Ningna Wang, The University of Texas at Dallas; Shiqing Xin, Shandong University; Shuangmin Chen, Qingdao University of Science and Technology; Mingyan Jiang, Shandong University; Xiaohu Guo, The University of Texas at Dallas; Wenping Wang, Texas A&M University; Changhe Tu, Shandong University
3D Gaussian Splatting for Real-time Radiance Field Rendering
The new technique enables real-time display of radiance fields with impressive visual quality, achieving a rendering rate of at least 30 frames per second. The researchers depict scenes using precise 3D Gaussians, facilitating efficient optimization processes.
The inclusion of visibility-aware rendering accelerates training, matching the speed of the fastest prior methods while maintaining comparable quality. Additionally, just one extra hour of training enhances the output to a state-of-the-art level of quality.
Authors: Bernhard Kerbl, Inria, Université Côte d’Azur; Georgios Kopanas, Inria, Université Côte d’Azur; Thomas Leimkuehler, Max-Planck-Institut für Informatik; George Drettakis, Inria, Université Côte d’Azur
DOC: Differentiable Optimal Control for Retargeting Motions Onto Legged Robots
The team of researchers at Disney Research have introduced a novel framework called Differentiable Optimal Control (DOC), which simplifies the calculation of analytical derivatives for optimal control and state trajectories based on user-defined parameters.
The work demonstrates its effectiveness by swiftly adapting motion capture and animation data onto a range of legged robots with differing proportions and mass distribution.
Ruben Grandia, Disney Research Imagineering; Farbod Farshidian, ETH Zürich; Espen Knoop, Disney Research Imagineering; Christian Schumacher, Disney Research Imagineering; Marco Hutter, ETH Zürich; Moritz Bächer, Disney Research Imagineering
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