no code implementations • 3 Oct 2018 • Omid Poursaeed, Guandao Yang, Aditya Prakash, Qiuren Fang, Hanqing Jiang, Bharath Hariharan, Serge Belongie
Estimating fundamental matrices is a classic problem in computer vision.
no code implementations • 8 Feb 2022 • Flora Yu Shen, Katie Luo, Guandao Yang, Harald Haraldsson, Serge Belongie
In this work, we address an important problem of optical see through (OST) augmented reality: non-negative image synthesis.
no code implementations • 27 Apr 2023 • Guandao Yang, Abhijit Kundu, Leonidas J. Guibas, Jonathan T. Barron, Ben Poole
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data.
no code implementations • NeurIPS 2023 • Mikaela Angelina Uy, Kiyohiro Nakayama, Guandao Yang, Rahul Krishna Thomas, Leonidas Guibas, Ke Li
Volume rendering requires evaluating an integral along each ray, which is numerically approximated with a finite sum that corresponds to the exact integral along the ray under piecewise constant volume density.
no code implementations • 5 Dec 2023 • Ryan Po, Guandao Yang, Kfir Aberman, Gordon Wetzstein
In this paper, we address a new problem called Modular Customization, with the goal of efficiently merging customized models that were fine-tuned independently for individual concepts.
no code implementations • 5 Apr 2024 • Yang Zheng, Qingqing Zhao, Guandao Yang, Wang Yifan, Donglai Xiang, Florian Dubost, Dmitry Lagun, Thabo Beeler, Federico Tombari, Leonidas Guibas, Gordon Wetzstein
This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop.
no code implementations • 26 Apr 2024 • IAn Huang, Guandao Yang, Leonidas Guibas
Specifically, we design a vision-based edit generator and state evaluator to work together to find the correct sequence of actions to achieve the goal.
1 code implementation • 10 Dec 2023 • Aditya Chetan, Guandao Yang, Zichen Wang, Steve Marschner, Bharath Hariharan
Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts.
1 code implementation • CVPR 2021 • Katie Luo, Guandao Yang, Wenqi Xian, Harald Haraldsson, Bharath Hariharan, Serge Belongie
In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image.
2 code implementations • ICLR 2018 • Felix Wu, Ni Lao, John Blitzer, Guandao Yang, Kilian Weinberger
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets.
1 code implementation • 9 Feb 2023 • Guandao Yang, Sagie Benaim, Varun Jampani, Kyle Genova, Jonathan T. Barron, Thomas Funkhouser, Bharath Hariharan, Serge Belongie
We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields.
1 code implementation • ECCV 2018 • Guandao Yang, Yin Cui, Serge Belongie, Bharath Hariharan
It is expensive to label images with 3D structure or precise camera pose.
1 code implementation • CVPR 2018 • Yin Cui, Guandao Yang, Andreas Veit, Xun Huang, Serge Belongie
To address these two challenges, we propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions.
3 code implementations • 26 Apr 2019 • Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa
Low precision operations can provide scalability, memory savings, portability, and energy efficiency.
1 code implementation • 8 Jan 2024 • Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, Gordon Wetzstein
These metrics lack the flexibility to generalize to different evaluation criteria and might not align well with human preferences.
1 code implementation • ECCV 2020 • Ruojin Cai, Guandao Yang, Hadar Averbuch-Elor, Zekun Hao, Serge Belongie, Noah Snavely, Bharath Hariharan
Point cloud generation thus amounts to moving randomly sampled points to high-density areas.
1 code implementation • NeurIPS 2021 • Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun
Most existing geometry processing algorithms use meshes as the default shape representation.
2 code implementations • 9 Oct 2019 • Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa
Low-precision training reduces computational cost and produces efficient models.
12 code implementations • ICCV 2019 • Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, Bharath Hariharan
Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
Ranked #4 on Point Cloud Generation on ShapeNet Car