no code implementations • 2 Dec 2023 • Pengsheng Guo, Hans Hao, Adam Caccavale, Zhongzheng Ren, Edward Zhang, Qi Shan, Aditya Sankar, Alexander G. Schwing, Alex Colburn, Fangchang Ma
Our analysis identifies the core of these challenges as the interaction among noise levels in the 2D diffusion process, the architecture of the diffusion network, and the 3D model representation.
no code implementations • 12 Oct 2023 • Xiaoming Zhao, Alex Colburn, Fangchang Ma, Miguel Angel Bautista, Joshua M. Susskind, Alexander G. Schwing
In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.
1 code implementation • CVPR 2023 • Chen Ziwen, Kaushik Patnaik, Shuangfei Zhai, Alvin Wan, Zhile Ren, Alex Schwing, Alex Colburn, Li Fuxin
To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task.
Ranked #4 on Instance Segmentation on Cityscapes val
1 code implementation • ICCV 2023 • Noah Stier, Anurag Ranjan, Alex Colburn, Yajie Yan, Liang Yang, Fangchang Ma, Baptiste Angles
Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency.
1 code implementation • ICCV 2023 • Noah Stier, Baptiste Angles, Liang Yang, Yajie Yan, Alex Colburn, Ming Chuang
Dense 3D reconstruction from RGB images traditionally assumes static camera pose estimates.
1 code implementation • 21 Jul 2022 • Xiaoming Zhao, Fangchang Ma, David Güera, Zhile Ren, Alexander G. Schwing, Alex Colburn
What is really needed to make an existing 2D GAN 3D-aware?
no code implementations • 12 Jul 2021 • Pengsheng Guo, Miguel Angel Bautista, Alex Colburn, Liang Yang, Daniel Ulbricht, Joshua M. Susskind, Qi Shan
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.
no code implementations • 14 Sep 2020 • Yue Liu, Alex Colburn, Mehlika Inanici
The proposed DNN model can faithfully predict high-quality annual panoramic luminance maps from one of the three options within 30 minutes training time: a) point-in-time luminance imagery spanning 5% of the year, when evenly distributed during daylight hours, b) one-month hourly imagery generated or collected continuously during daylight hours around the equinoxes (8% of the year); or c) 9 days of hourly data collected around the spring equinox, summer and winter solstices (2. 5% of the year) all suffice to predict the luminance maps for the rest of the year.
1 code implementation • ICML 2020 • Emilien Dupont, Miguel Angel Bautista, Alex Colburn, Aditya Sankar, Carlos Guestrin, Josh Susskind, Qi Shan
We propose a framework for learning neural scene representations directly from images, without 3D supervision.
no code implementations • 19 Nov 2019 • Deepali Aneja, Alex Colburn, Gary Faigin, Linda Shapiro, Barbara Mones
We present DeepExpr, a novel expression transfer system from humans to multiple stylized characters via deep learning.
3 code implementations • 9 Oct 2019 • Chuhang Zou, Jheng-Wei Su, Chi-Han Peng, Alex Colburn, Qi Shan, Peter Wonka, Hung-Kuo Chu, Derek Hoiem
Recent approaches for predicting layouts from 360 panoramas produce excellent results.
2 code implementations • CVPR 2018 • Chuhang Zou, Alex Colburn, Qi Shan, Derek Hoiem
We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e. g. L-shape room).