Search Results for author: Xinchen Yan

Found 19 papers, 10 papers with code

Waymo Open Dataset: Panoramic Video Panoptic Segmentation

no code implementations15 Jun 2022 Jieru Mei, Alex Zihao Zhu, Xinchen Yan, Hang Yan, Siyuan Qiao, Yukun Zhu, Liang-Chieh Chen, Henrik Kretzschmar, Dragomir Anguelov

We therefore present the Waymo Open Dataset: Panoramic Video Panoptic Segmentation Dataset, a large-scale dataset that offers high-quality panoptic segmentation labels for autonomous driving.

Autonomous Driving Benchmark +3

S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling

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.

Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving

no code implementations17 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.

Adversarial Robustness Denoising

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

no code implementations7 Jan 2021 Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun

On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.

Motion Forecasting

ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds

no code implementations24 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.

PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions

1 code implementation ECCV 2020 Kaichun Mo, He Wang, Xinchen Yan, Leonidas J. Guibas

3D generative shape modeling is a fundamental research area in computer vision and interactive computer graphics, with many real-world applications.

3D Shape Generation Computer Vision

Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks

no code implementations21 Jun 2019 Xinchen Yan, Mohi Khansari, Jasmine Hsu, Yuanzheng Gong, Yunfei Bai, Sören Pirk, Honglak Lee

Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data.

3D Shape Representation Robotic Grasping

SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing

1 code implementation19 Jun 2019 Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, Bo Li

In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples".

Face Recognition Face Verification

MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics

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.

Human Dynamics motion prediction

Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations

1 code implementation24 Aug 2017 Xinchen Yan, Jasmine Hsu, Mohi Khansari, Yunfei Bai, Arkanath Pathak, Abhinav Gupta, James Davidson, Honglak Lee

Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations.

3D Geometry Prediction 3D Shape Modeling +1

Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision

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.

3D Object Reconstruction Computer Vision

Deep Variational Canonical Correlation Analysis

no code implementations11 Oct 2016 Weiran Wang, Xinchen Yan, Honglak Lee, Karen Livescu

We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear observation models parameterized by deep neural networks.

MULTI-VIEW LEARNING

Generative Adversarial Text to Image Synthesis

38 code implementations17 May 2016 Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee

Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal.

Adversarial Text Text-to-Image Generation

Learning Structured Output Representation using Deep Conditional Generative Models

1 code implementation NeurIPS 2015 Kihyuk Sohn, Honglak Lee, Xinchen Yan

The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows a fast prediction using stochastic feed-forward inference.

Computer Vision Semantic Segmentation +1

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