Michael J. Black

Publications
Influence

A Database and Evaluation Methodology for Optical Flow

S. Baker, D. Scharstein, J. Lewis, S. Roth, Michael J. Black, R. Szeliski
Computer Science
IEEE 11th International Conference on Computer…
1 March 2011

The quantitative evaluation of optical flow algorithms by Barron et al. (1994) led to significant advances in performance. Expand

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A Naturalistic Open Source Movie for Optical Flow Evaluation

Daniel J. Butler, J. Wulff, G. B. Stanley, Michael J. Black
Computer Science
ECCV
7 October 2012

We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. Expand

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SMPL: a skinned multi-person linear model

M. Loper, Naureen Mahmood, J. Romero, Gerard Pons-Moll, Michael J. Black
Computer Science
ACM Trans. Graph.
4 November 2015

We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Expand

Secrets of optical flow estimation and their principles

Deqing Sun, S. Roth, Michael J. Black
Mathematics, Computer Science
IEEE Computer Society Conference on Computer…
13 June 2010

We derive a new objective that formalizes the median filtering heuristic. Expand

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End-to-End Recovery of Human Shape and Pose

Angjoo Kanazawa, Michael J. Black, D. Jacobs, Jitendra Malik
Computer Science
IEEE/CVF Conference on Computer Vision and…
18 December 2017

We describe Human Mesh Recovery (HMR), an end-to-end framework for reconstructing a full 3D mesh of a human body from a single RGB image. Expand

Towards Understanding Action Recognition

Hueihan Jhuang, Juergen Gall, S. Zuffi, C. Schmid, Michael J. Black
Computer Science
IEEE International Conference on Computer Vision
1 December 2013

Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video… Expand

The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields

Michael J. Black, P. Anandan
Computer Science
Comput. Vis. Image Underst.
1996

This paper presents a framework based onrobust estimation that addresses violations of the brightness constancy and spatial smoothness assumptions caused by multiple motions, and provides examples with natural and synthetic image sequences. Expand

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HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion

L. Sigal, A. O. Balan, Michael J. Black
Computer Science
International Journal of Computer Vision
1 March 2010

We present data obtained using a hardware system that is able to capture synchronized video and ground-truth 3D motion. Expand

Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

Federica Bogo, Angjoo Kanazawa, Christoph Lassner, P. Gehler, J. Romero, Michael J. Black
Computer Science
ECCV
27 July 2016

We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. Expand

On Human Motion Prediction Using Recurrent Neural Networks

J. Martinez, Michael J. Black, J. Romero
Computer Science
IEEE Conference on Computer Vision and Pattern…
6 May 2017

We propose three changes to the standard RNN models typically used for human motion, which results in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction. Expand

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