Search Results for author:
Found 12 papers, 7 papers with code
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
This paper considers the task of articulated human pose estimation of multiple people in real world images.
Ranked #2 on
Multi-Person Pose Estimation
on WAF
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.
Ranked #1 on
Multi-Person Pose Estimation
on WAF
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset.
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications
In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.
ArtTrack: Articulated Multi-person Tracking in the Wild
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos.
Ranked #7 on
Keypoint Detection
on MPII Multi-Person
EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)
Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center.
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications
In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time.
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
In this work, we aim to further advance the state of the art by establishing "PoseTrack", a new large-scale benchmark for video-based human pose estimation and articulated tracking, and bringing together the community of researchers working on visual human analysis.
Ranked #3 on
Multi-Person Pose Estimation
on PoseTrack2017
Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
We address the problem of learning accurate 3D shape and camera pose from a collection of unlabeled category-specific images.
360-Degree Textures of People in Clothing from a Single Image
In order to learn our model in a common UV-space, we non-rigidly register the SMPL model to thousands of 3D scans, effectively encoding textures and geometries as images in correspondence.
Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views
We present a system for automatic converting of 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects.
SNeS: Learning Probably Symmetric Neural Surfaces from Incomplete Data
We present a method for the accurate 3D reconstruction of partly-symmetric objects.
