Search Results for author:
Found 14 papers, 2 papers with code
MonoNHR: Monocular Neural Human Renderer
Existing neural human rendering methods struggle with a single image input due to the lack of information in invisible areas and the depth ambiguity of pixels in visible areas.
Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images
We propose a method to leverage self-supervised methods that provides training signal in the absence of confident pseudo-labels.
Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
While there exists many methods for manipulating rigid objects with parallel-jaw grippers, grasping with multi-finger robotic hands remains a quite unexplored research topic.
Progressive Skeletonization: Trimming more fat from a network at initialization
Recent studies have shown that skeletonization (pruning parameters) of networks \textit{at initialization} provides all the practical benefits of sparsity both at inference and training time, while only marginally degrading their performance.
Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images
In this paper, we tackle the problem of 3D human shape estimation from single RGB images.
LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images
We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images.
3D Human Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+1
LCR-Net: Localization-Classification-Regression for Human Pose
We propose an end-to-end architecture for joint 2D and 3D human pose estimation in natural images.
Ranked #4 on
3D Multi-Person Pose Estimation (root-relative)
on MuPoTS-3D
(MPJPE metric)
3D Human Pose Estimation
3D Multi-Person Pose Estimation (absolute)
+3
The Open World of Micro-Videos
Micro-videos are six-second videos popular on social media networks with several unique properties.
Depth-Based Hand Pose Estimation: Data, Methods, and Challenges
To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes.
Understanding Everyday Hands in Action From RGB-D Images
We analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification.
First-Person Pose Recognition Using Egocentric Workspaces
In egocentric views, hands and arms are observable within a well defined volume in front of the camera.
Depth-based hand pose estimation: methods, data, and challenges
To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes.
Egocentric Pose Recognition in Four Lines of Code
We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera.
3D Hand Pose Detection in Egocentric RGB-D Images
We focus on the task of everyday hand pose estimation from egocentric viewpoints.
