Search Results for author:
Found 15 papers, 6 papers with code
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World
Building an interactive AI assistant that can perceive, reason, and collaborate with humans in the real world has been a long-standing pursuit in the AI community.
CaSAR: Contact-aware Skeletal Action Recognition
Skeletal Action recognition from an egocentric view is important for applications such as interfaces in AR/VR glasses and human-robot interaction, where the device has limited resources.
MCTS with Refinement for Proposals Selection Games in Scene Understanding
We also introduce a novel differentiable method for rendering the polygonal shapes of these proposals.
Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation
We propose a robust and accurate method for estimating the 3D poses of two hands in close interaction from a single color image.
Ranked #4 on
hand-object pose
on HO-3D
MonteFloor: Extending MCTS for Reconstructing Accurate Large-Scale Floor Plans
For this step, we propose a novel differentiable method for rendering the polygonal shapes of these proposals.
ALCN: Adaptive Local Contrast Normalization
We show that our method significantly outperforms standard normalization methods and would also be appear to be universal since it does not have to be re-trained for each new application.
Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction
To address these issues, we designed a public challenge (HANDS'19) to evaluate the abilities of current 3D hand pose estimators (HPEs) to interpolate and extrapolate the poses of a training set.
General 3D Room Layout from a Single View by Render-and-Compare
In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate.
HOnnotate: A method for 3D Annotation of Hand and Object Poses
This dataset is currently made of 77, 558 frames, 68 sequences, 10 persons, and 10 objects.
Domain Transfer for 3D Pose Estimation from Color Images without Manual Annotations
We introduce a novel learning method for 3D pose estimation from color images.
Making Deep Heatmaps Robust to Partial Occlusions for 3D Object Pose Estimation
We introduce a novel method for robust and accurate 3D object pose estimation from a single color image under large occlusions.
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
The ability of using synthetic images for training a Deep Network is extremely valuable as it is easy to create a virtually infinite training set made of such images, while capturing and annotating real images can be very cumbersome.
ALCN: Meta-Learning for Contrast Normalization Applied to Robust 3D Pose Estimation
We therefore propose a novel illumination normalization method that lets us learn to detect objects and estimate their 3D pose under challenging illumination conditions from very few training samples.
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
We introduce a novel method for 3D object detection and pose estimation from color images only.
Ranked #19 on
6D Pose Estimation using RGB
on LineMOD
A Novel Representation of Parts for Accurate 3D Object Detection and Tracking in Monocular Images
We present a method that estimates in real-time and under challenging conditions the 3D pose of a known object.
