409 citations · 1.1k across the 16 of their papers we have counts for
18 papers
Learned Neural Physics Simulation for Articulated 3D Human Pose Reconstruction
Mykhaylo Andriluka, Baruch Tabanpour, C. Daniel Freeman +1
We propose a novel neural network approach, LARP (Learned Articulated Rigid body Physics), to model the dynamics of articulated human motion with contact. Our goal is to develop a…
Transformer-Based Learned Optimization
Erik Gärtner, Luke Metz, Mykhaylo Andriluka +2
We propose a new approach to learned optimization where we represent the computation of an optimizer's update step using a neural network. The parameters of the optimizer are then…
Trajectory Optimization for Physics-Based Reconstruction of 3d Human Pose from Monocular Video
Erik Gärtner, Mykhaylo Andriluka, Hongyi Xu +1
We focus on the task of estimating a physically plausible articulated human motion from monocular video. Existing approaches that do not consider physics often produce temporally i…
Differentiable Dynamics for Articulated 3d Human Motion Reconstruction
Erik Gärtner, Mykhaylo Andriluka, Erwin Coumans +1
We introduce DiffPhy, a differentiable physics-based model for articulated 3d human motion reconstruction from video. Applications of physics-based reasoning in human motion analys…
Efficient Full Image Interactive Segmentation by Leveraging Within-image Appearance Similarity
Mykhaylo Andriluka, Stefano Pellegrini, Stefan Popov +1
We propose a new approach to interactive full-image semantic segmentation which enables quickly collecting training data for new datasets with previously unseen semantic classes (A…
Panoptic Image Annotation with a Collaborative Assistant
Jasper R. R. Uijlings, Mykhaylo Andriluka, Vittorio Ferrari
This paper aims to reduce the time to annotate images for panoptic segmentation, which requires annotating segmentation masks and class labels for all object instances and stuff re…
Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation
Mykhaylo Andriluka, Jasper R. R. Uijlings, Vittorio Ferrari
We introduce Fluid Annotation, an intuitive human-machine collaboration interface for annotating the class label and outline of every object and background region in an image. Flui…
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Mykhaylo Andriluka, Umar Iqbal, Eldar Insafutdinov +4
Human poses and motions are important cues for analysis of videos with people and there is strong evidence that representations based on body pose are highly effective for a variet…
ArtTrack: Articulated Multi-person Tracking in the Wild
Eldar Insafutdinov, Mykhaylo Andriluka, Leonid Pishchulin +4
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos. Our starting point is a model that resembles existing architectures for si…
Multi-Person Tracking by Multicut and Deep Matching
Siyu Tang, Bjoern Andres, Mykhaylo Andriluka +1
In [1], we proposed a graph-based formulation that links and clusters person hypotheses over time by solving a minimum cost subgraph multicut problem. In this paper, we modify and…
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres +2
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) i…
DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation
Leonid Pishchulin, Eldar Insafutdinov, Siyu Tang +4
This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and po…
End-to-end people detection in crowded scenes
Russell Stewart, Mykhaylo Andriluka
Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding…
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Serena Yeung, Olga Russakovsky, Ning Jin +3
Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multip…
An Empirical Evaluation of Deep Learning on Highway Driving
Brody Huval, Tao Wang, Sameep Tandon +10
Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical ev…
Recognizing Fine-Grained and Composite Activities using Hand-Centric Features and Script Data
Marcus Rohrbach, Anna Rohrbach, Michaela Regneri +4
Activity recognition has shown impressive progress in recent years. However, the challenges of detecting fine-grained activities and understanding how they are combined into compos…
Fine-grained Activity Recognition with Holistic and Pose based Features
Leonid Pishchulin, Mykhaylo Andriluka, Bernt Schiele
Holistic methods based on dense trajectories are currently the de facto standard for recognition of human activities in video. Whether holistic representations will sustain or will…
Learning Human Pose Estimation Features with Convolutional Networks
Arjun Jain, Jonathan Tompson, Mykhaylo Andriluka +2
This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level f…