1.1k citations · 3.7k across the 97 of their papers we have counts for
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End-to-end Learning for Graph Decomposition
Jie Song, Bjoern Andres, Michael Black +2
We propose a novel end-to-end trainable framework for the graph decomposition problem. The minimum cost multicut problem is first converted to an unconstrained binary cubic formula…
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences
Nikolas Hesse, Sergi Pujades, Michael J. Black +3
Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and ar…
Deep Inertial Poser: Learning to Reconstruct Human Pose from Sparse Inertial Measurements in Real Time
Yinghao Huang, Manuel Kaufmann, Emre Aksan +3
We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so…
Temporal Interpolation as an Unsupervised Pretraining Task for Optical Flow Estimation
Jonas Wulff, Michael J. Black
The difficulty of annotating training data is a major obstacle to using CNNs for low-level tasks in video. Synthetic data often does not generalize to real videos, while unsupervis…
Generating 3D faces using Convolutional Mesh Autoencoders
Anurag Ranjan, Timo Bolkart, Soubhik Sanyal +1
Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as…
Learning Human Optical Flow
Anurag Ranjan, Javier Romero, Michael J. Black
The optical flow of humans is well known to be useful for the analysis of human action. Given this, we devise an optical flow algorithm specifically for human motion and show that…