no code implementations • 18 Jan 2021 • Jerry Liu, Wenyuan Zeng, Raquel Urtasun, Ersin Yumer
An intelligent agent operating in the real-world must balance achieving its goal with maintaining the safety and comfort of not only itself, but also other participants within the surrounding scene.
no code implementations • 18 Jan 2021 • Min Bai, Shenlong Wang, Kelvin Wong, Ersin Yumer, Raquel Urtasun
In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV).
no code implementations • 17 Jan 2021 • James Tu, Huichen Li, Xinchen Yan, Mengye Ren, Yun Chen, Ming Liang, Eilyan Bitar, Ersin Yumer, Raquel Urtasun
Yet, there have been limited studies on the adversarial robustness of multi-modal models that fuse LiDAR features with image features.
no code implementations • CVPR 2021 • Ze Yang, Shenlong Wang, Sivabalan Manivasagam, Zeng Huang, Wei-Chiu Ma, Xinchen Yan, Ersin Yumer, Raquel Urtasun
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.
no code implementations • CVPR 2021 • Yun Chen, Frieda Rong, Shivam Duggal, Shenlong Wang, Xinchen Yan, Sivabalan Manivasagam, Shangjie Xue, Ersin Yumer, Raquel Urtasun
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving.
no code implementations • 16 Jan 2021 • Abbas Sadat, Sean Segal, Sergio Casas, James Tu, Bin Yang, Raquel Urtasun, Ersin Yumer
Our experiments on a wide range of tasks and models show that the proposed curation pipeline is able to select datasets that lead to better generalization and higher performance.
no code implementations • 12 Nov 2020 • Sean Segal, Eric Kee, Wenjie Luo, Abbas Sadat, Ersin Yumer, Raquel Urtasun
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data.
no code implementations • ICML 2020 • Cong Han Lim, Raquel Urtasun, Ersin Yumer
We show that, under certain conditions on the algorithm parameters, LayerCert provably reduces the number and size of the convex programs that one needs to solve compared to GeoCert.
no code implementations • 24 May 2020 • Kibok Lee, Zhuoyuan Chen, Xinchen Yan, Raquel Urtasun, Ersin Yumer
Our shape-aware adversarial attacks are orthogonal to existing point cloud based attacks and shed light on the vulnerability of 3D deep neural networks.
1 code implementation • 9 May 2020 • Yanran Guan, Han Liu, Kun Liu, Kangxue Yin, Ruizhen Hu, Oliver van Kaick, Yan Zhang, Ersin Yumer, Nathan Carr, Radomir Mech, Hao Zhang
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
no code implementations • 10 Oct 2019 • Abbas Sadat, Mengye Ren, Andrei Pokrovsky, Yen-Chen Lin, Ersin Yumer, Raquel Urtasun
The motion planners used in self-driving vehicles need to generate trajectories that are safe, comfortable, and obey the traffic rules.
1 code implementation • ICLR 2020 • Mengtian Li, Ersin Yumer, Deva Ramanan
We also revisit existing approaches for fast convergence and show that budget-aware learning schedules readily outperform such approaches under (the practical but under-explored) budgeted training setting.
1 code implementation • CVPR 2019 • Yuwen Xiong, Renjie Liao, Hengshuang Zhao, Rui Hu, Min Bai, Ersin Yumer, Raquel Urtasun
More importantly, we introduce a parameter-free panoptic head which solves the panoptic segmentation via pixel-wise classification.
Ranked #3 on Panoptic Segmentation on Indian Driving Dataset
3 code implementations • 2 Jan 2019 • Mengtian Li, Zhe Lin, Radomir Mech, Ersin Yumer, Deva Ramanan
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.
no code implementations • ECCV 2018 • Lingyu Wei, Liwen Hu, Vladimir Kim, Ersin Yumer, Hao Li
To handle the diversity of hairstyles and its appearance complexity, we disentangle hair structure, color, and illumination properties using a sequential GAN architecture and a semi-supervised training approach.
1 code implementation • ECCV 2018 • Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, Honglak Lee
Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode.
Ranked #8 on Human Pose Forecasting on Human3.6M (ADE metric)
1 code implementation • ECCV 2018 • Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, Ming-Hsuan Yang
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
no code implementations • 20 Jun 2018 • Aron Monszpart, Paul Guerrero, Duygu Ceylan, Ersin Yumer, Niloy J. Mitra
A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video.
no code implementations • 22 May 2018 • Minh Vo, Ersin Yumer, Kalyan Sunkavalli, Sunil Hadap, Yaser Sheikh, Srinivasa Narasimhan
Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams.
1 code implementation • CVPR 2018 • Chen Liu, Jimei Yang, Duygu Ceylan, Ersin Yumer, Yasutaka Furukawa
The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image.
Ranked #2 on Plane Instance Segmentation on NYU Depth v2
2 code implementations • ECCV 2018 • Gül Varol, Duygu Ceylan, Bryan Russell, Jimei Yang, Ersin Yumer, Ivan Laptev, Cordelia Schmid
Human shape estimation is an important task for video editing, animation and fashion industry.
Ranked #3 on 3D Human Pose Estimation on Surreal (using extra training data)
2 code implementations • CVPR 2018 • Chen-Hsuan Lin, Ersin Yumer, Oliver Wang, Eli Shechtman, Simon Lucey
We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image.
1 code implementation • NeurIPS 2017 • Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer, Katerina Fragkiadaki
In this work, we propose a learning based motion capture model for single camera input.
Ranked #2 on 3D Human Reconstruction on Surreal
no code implementations • 28 Oct 2017 • Moos Hueting, Pradyumna Reddy, Vladimir Kim, Ersin Yumer, Nathan Carr, Niloy Mitra
Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc.
2 code implementations • ICCV 2017 • Chuhang Zou, Ersin Yumer, Jimei Yang, Duygu Ceylan, Derek Hoiem
The success of various applications including robotics, digital content creation, and visualization demand a structured and abstract representation of the 3D world from limited sensor data.
no code implementations • ICCV 2017 • Guilin Liu, Duygu Ceylan, Ersin Yumer, Jimei Yang, Jyh-Ming Lien
We propose an end-to-end network architecture that replicates the forward image formation process to accomplish this task.
no code implementations • CVPR 2017 • Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
no code implementations • 14 Jun 2017 • Haibin Huang, Evangelos Kalogerakis, Siddhartha Chaudhuri, Duygu Ceylan, Vladimir G. Kim, Ersin Yumer
We present a new local descriptor for 3D shapes, directly applicable to a wide range of shape analysis problems such as point correspondences, semantic segmentation, affordance prediction, and shape-to-scan matching.
no code implementations • CVPR 2017 • Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, C. Lee Giles
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
no code implementations • 5 May 2017 • Jun Li, Kai Xu, Siddhartha Chaudhuri, Ersin Yumer, Hao Zhang, Leonidas Guibas
We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures.
2 code implementations • CVPR 2017 • Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, Dimitris Samaras
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.
no code implementations • 1 Apr 2017 • Marc-André Gardner, Kalyan Sunkavalli, Ersin Yumer, Xiaohui Shen, Emiliano Gambaretto, Christian Gagné, Jean-François Lalonde
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene.
2 code implementations • CVPR 2017 • Eunbyung Park, Jimei Yang, Ersin Yumer, Duygu Ceylan, Alexander C. Berg
Instead of taking a 'blank slate' approach, we first explicitly infer the parts of the geometry visible both in the input and novel views and then re-cast the remaining synthesis problem as image completion.
no code implementations • CVPR 2017 • Yinda Zhang, Shuran Song, Ersin Yumer, Manolis Savva, Joon-Young Lee, Hailin Jin, Thomas Funkhouser
One of the bottlenecks in training for better representations is the amount of available per-pixel ground truth data that is required for core scene understanding tasks such as semantic segmentation, normal prediction, and object edge detection.
2 code implementations • NeurIPS 2016 • Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, Honglak Lee
We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes.