Search Results for author:
Found 28 papers, 19 papers with code
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots.
Target-Referenced Reactive Grasping for Dynamic Objects
Reactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics.
Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions
In daily HOIs, humans often interact with a variety of objects, e. g., holding and touching dozens of household items in cleaning.
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
Accurate whole-body multi-person pose estimation and tracking is an important yet challenging topic in computer vision.
X-NeRF: Explicit Neural Radiance Field for Multi-Scene 360$^{\circ} $ Insufficient RGB-D Views
In this paper, we focus on a rarely discussed but important setting: can we train one model that can represent multiple scenes, with 360$^\circ $ insufficient views and RGB-D images?
Unseen Object 6D Pose Estimation: A Benchmark and Baselines
In this paper, we propose a new task that enables and facilitates algorithms to estimate the 6D pose estimation of novel objects during testing.
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline
However, the majority of current grasping algorithms would fail in this case since they heavily rely on the depth image, while ordinary depth sensors usually fail to produce accurate depth information for transparent objects owing to the reflection and refraction of light.
Ranked #1 on
Transparent Object Depth Estimation
on TransCG
HAKE: A Knowledge Engine Foundation for Human Activity Understanding
Human activity understanding is of widespread interest in artificial intelligence and spans diverse applications like health care and behavior analysis.
Human Trajectory Prediction With Momentary Observation
Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots.
SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping
Meanwhile, we propose a method to predict numerous suction poses from an RGB-D image of a cluttered scene and demonstrate our superiority against several previous methods.
Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis
Multimodal prediction results are essential for trajectory prediction task as there is no single correct answer for the future.
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
In the first stage, an encoder-decoder like convolutional neural network Angle-View Net(AVN) is proposed to predict the SO(3) orientation of the gripper at every location of the image.
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
To quickly detect graspness in practice, we develop a neural network named graspness model to approximate the searching process.
Ranked #3 on
Robotic Grasping
on GraspNet-1Billion
DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection
On the other hand, existing one-stage methods mainly focus on the union regions of interactions, which introduce unnecessary visual information as disturbances to HOI detection.
Ranked #15 on
Human-Object Interaction Detection
on V-COCO
DecAug: Augmenting HOI Detection via Decomposition
Human-object interaction (HOI) detection requires a large amount of annotated data.
Ranked #68 on
Domain Generalization
on PACS
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping
In this work, we contribute a large-scale grasp pose detection dataset with a unified evaluation system.
Ranked #6 on
Robotic Grasping
on GraspNet-1Billion
PaStaNet: Toward Human Activity Knowledge Engine
In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.
Ranked #3 on
Human-Object Interaction Detection
on HICO
GraspNet: A Large-Scale Clustered and Densely Annotated Dataset for Object Grasping
Object grasping is critical for many applications, which is also a challenging computer vision problem.
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
With the guidance of such map, we boost the performance of R101-Mask R-CNN on instance segmentation from 35. 7 mAP to 37. 9 mAP without modifying the backbone or network structure.
Ranked #78 on
Instance Segmentation
on COCO test-dev
Cross-Domain Adaptation for Animal Pose Estimation
Therefore, the easily available human pose dataset, which is of a much larger scale than our labeled animal dataset, provides important prior knowledge to boost up the performance on animal pose estimation.
HAKE: Human Activity Knowledge Engine
To address these and promote the activity understanding, we build a large-scale Human Activity Knowledge Engine (HAKE) based on the human body part states.
Ranked #2 on
Human-Object Interaction Detection
on HICO
(using extra training data)
Estimating 6D Pose From Localizing Designated Surface Keypoints
In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.
Ranked #17 on
6D Pose Estimation using RGB
on LineMOD
CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark
In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms.
Ranked #6 on
Multi-Person Pose Estimation
on OCHuman
Transferable Interactiveness Knowledge for Human-Object Interaction Detection
On account of the generalization of interactiveness, interactiveness network is a transferable knowledge learner and can be cooperated with any HOI detection models to achieve desirable results.
Ranked #29 on
Human-Object Interaction Detection
on V-COCO
Pairwise Body-Part Attention for Recognizing Human-Object Interactions
We propose a new pairwise body-part attention model which can learn to focus on crucial parts, and their correlations for HOI recognition.
Ranked #5 on
Human-Object Interaction Detection
on HICO
Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer
In this paper, we present a novel method to generate synthetic human part segmentation data using easily-obtained human keypoint annotations.
Ranked #4 on
Human Part Segmentation
on PASCAL-Part
(using extra training data)
Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation
In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation.
Ranked #1 on
3D Absolute Human Pose Estimation
on Human3.6M
(Average MPJPE (mm) metric)
RMPE: Regional Multi-person Pose Estimation
In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes.
Ranked #1 on
Pose Estimation
on UAV-Human
