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Jun Gao. I am a Ph.D. candidate at the University of Toronto, advised by Prof. Sanja Fidler, and I am affiliated with the Vector Institute. I am also a research scientist at NVIDIA.
Articles 1–20. University of Toronto, NVIDIA - Cited by 3,951 - Computer Vision - Machine Learning.
Jun Gao. jungao@cs.toronto.edu ⋄ Homepage ⋄ Google Scholar ⋄ (+1) 437-985-2877 I am interested in computer vision, computer graphics and machine learning. I develop 3D generative AI models to create realistic, high-quality and diverse 3D content for reconstructing, generating and simulating 3D worlds.
REAM♯: An enhancement approach to reference-based evaluation metrics for open-domain dialog generation. J Gao, W Bi, R Xu, S Shi. ACL'2021 (Findings) , 2021. 9. 2021. Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset.
Jul 4, 2023 · K. Jun Gao. As of 4 July 2023. I’m a research student and fourth-year undergraduate at the University of Toronto. I am pursuing a degree in computer science, bioinformatics and computational biology, with a math minor and focus in theoretical CS.
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis. Your browser does not support the video tag. We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels or noisy point cloud.
Sep 22, 2022 · View a PDF of the paper titled GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images, by Jun Gao and 8 other authors
