93 citations · 706 across the 49 of their papers we have counts for
22 papers · 1 filter
ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation
Zehao Zhu, Jiashun Wang, Yuzhe Qin +3
We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation. We first collect a dataset using visual te…
Learning Continuous Environment Fields via Implicit Functions
Xueting Li, Shalini De Mello, Xiaolong Wang +3
We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory. We demonstrate that…
Multi-Person 3D Motion Prediction with Multi-Range Transformers
Jiashun Wang, Huazhe Xu, Medhini Narasimhan +1
We propose a novel framework for multi-person 3D motion trajectory prediction. Our key observation is that a human's action and behaviors may highly depend on the other persons aro…
Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild
Jianglong Ye, Yuntao Chen, Naiyan Wang +1
Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit…
Video Autoencoder: self-supervised disentanglement of static 3D structure and motion
Zihang Lai, Sifei Liu, Alexei A. Efros +1
A video autoencoder is proposed for learning disentan- gled representations of 3D structure and camera pose from videos in a self-supervised manner. Relying on temporal continuity…
Test-Time Personalization with a Transformer for Human Pose Estimation
Yizhuo Li, Miao Hao, Zonglin Di +2
We propose to personalize a human pose estimator given a set of test images of a person without using any manual annotations. While there is a significant advancement in human pose…
Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time
Shaowei Liu, Hanwen Jiang, Jiarui Xu +2
Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations ar…
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency
Haiping Wu, Xiaolong Wang
Recent works have advanced the performance of self-supervised representation learning by a large margin. The core among these methods is intra-image invariance learning. Two differ…
A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation
Jiteng Mu, Weichao Qiu, Adam Kortylewski +3
Recent work has made significant progress on using implicit functions, as a continuous representation for 3D rigid object shape reconstruction. However, much less effort has been d…
Hand-Object Contact Consistency Reasoning for Human Grasps Generation
Hanwen Jiang, Shaowei Liu, Jiashun Wang +1
While predicting robot grasps with parallel jaw grippers have been well studied and widely applied in robot manipulation tasks, the study on natural human grasp generation with a m…
Robust Object Detection via Instance-Level Temporal Cycle Confusion
Xin Wang, Thomas E. Huang, Benlin Liu +4
Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications.…
Rethinking Self-supervised Correspondence Learning: A Video Frame-level Similarity Perspective
Jiarui Xu, Xiaolong Wang
Learning a good representation for space-time correspondence is the key for various computer vision tasks, including tracking object bounding boxes and performing video object pixe…
Region Similarity Representation Learning
Tete Xiao, Colorado J Reed, Xiaolong Wang +2
We present Region Similarity Representation Learning (ReSim), a new approach to self-supervised representation learning for localization-based tasks such as object detection and se…
Learning Continuous Image Representation with Local Implicit Image Function
Yinbo Chen, Sifei Liu, Xiaolong Wang
How to represent an image? While the visual world is presented in a continuous manner, machines store and see the images in a discrete way with 2D arrays of pixels. In this paper,…
What Should Not Be Contrastive in Contrastive Learning
Tete Xiao, Xiaolong Wang, Alexei A. Efros +1
Recent self-supervised contrastive methods have been able to produce impressive transferable visual representations by learning to be invariant to different data augmentations. How…
Learning Long-term Visual Dynamics with Region Proposal Interaction Networks
Haozhi Qi, Xiaolong Wang, Deepak Pathak +2
Learning long-term dynamics models is the key to understanding physical common sense. Most existing approaches on learning dynamics from visual input sidestep long-term predictions…
Compositional Video Synthesis with Action Graphs
Amir Bar, Roei Herzig, Xiaolong Wang +4
Videos of actions are complex signals containing rich compositional structure in space and time. Current video generation methods lack the ability to condition the generation on mu…
Class-Aware Domain Adaptation for Improving Adversarial Robustness
Xianxu Hou, Jingxin Liu, Bolei Xu +3
Recent works have demonstrated convolutional neural networks are vulnerable to adversarial examples, i.e., inputs to machine learning models that an attacker has intentionally desi…
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning
Yinbo Chen, Zhuang Liu, Huijuan Xu +2
Meta-learning has been the most common framework for few-shot learning in recent years. It learns the model from collections of few-shot classification tasks, which is believed to…
Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks
Joanna Materzynska, Tete Xiao, Roei Herzig +3
Human action is naturally compositional: humans can easily recognize and perform actions with objects that are different from those used in training demonstrations. In this paper,…
Joint-task Self-supervised Learning for Temporal Correspondence
Xueting Li, Sifei Liu, Shalini De Mello +3
This paper proposes to learn reliable dense correspondence from videos in a self-supervised manner. Our learning process integrates two highly related tasks: tracking large image r…
Putting Humans in a Scene: Learning Affordance in 3D Indoor Environments
Xueting Li, Sifei Liu, Kihwan Kim +3
Affordance modeling plays an important role in visual understanding. In this paper, we aim to predict affordances of 3D indoor scenes, specifically what human poses are afforded by…