Institute Homepage
Institute Homepage Sign In

Publications

DEPARTMENTS

Emperical Interference

Haptic Intelligence

Modern Magnetic Systems

Perceiving Systems

Physical Intelligence

Robotic Materials

Social Foundations of Computation

Research Groups

Autonomous Vision

Autonomous Learning

Bioinspired Autonomous Miniature Robots

Dynamic Locomotion

Embodied Vision

Human Aspects of Machine Learning

Intelligent Control Systems

Learning and Dynamical Systems

Locomotion in Biorobotic and Somatic Systems

Micro, Nano, and Molecular Systems

Movement Generation and Control

Neural Capture and Synthesis

Physics for Inference and Optimization

Organizational Leadership and Diversity

Probabilistic Learning Group

Topics

Robot Learning

Conference Paper

2022

Autonomous Learning

Robotics

AI

Career

Award

Perceiving Systems Technical Report Model transport: towards scalable transfer learning on manifolds - supplemental material Freifeld, O., Hauberg, S., Black, M. J. (9), April 2014
This technical report is complementary to "Model Transport: Towards Scalable Transfer Learning on Manifolds" and contains proofs, explanation of the attached video (visualization of bases from the body shape experiments), and high-resolution images of select results of individual reconstructions from the shape experiments. It is identical to the supplemental mate- rial submitted to the Conference on Computer Vision and Pattern Recognition (CVPR 2014) on November 2013.
PDF BibTeX
Thumb ticker lg modeltransport
Perceiving Systems Technical Report Puppet Flow Zuffi, S., Black, M. J. (7), Max Planck Institute for Intelligent Systems, October 2013
We introduce Puppet Flow (PF), a layered model describing the optical flow of a person in a video sequence. We consider video frames composed by two layers: a foreground layer corresponding to a person, and background. We model the background as an affine flow field. The foreground layer, being a moving person, requires reasoning about the articulated nature of the human body. We thus represent the foreground layer with the Deformable Structures model (DS), a parametrized 2D part-based human body representation. We call the motion field defined through articulated motion and deformation of the DS model, a Puppet Flow. By exploiting the DS representation, Puppet Flow is a parametrized optical flow field, where parameters are the person's pose, gender and body shape.
pdf BibTeX
Thumb ticker lg implied flow whue
Perceiving Systems Technical Report A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them Sun, D., Roth, S., Black, M. J. (CS-10-03), Brown University, Department of Computer Science, January 2013 pdf BibTeX
Thumb ticker lg secretstr
Perceiving Systems Technical Report Coregistration: Supplemental Material Hirshberg, D., Loper, M., Rachlin, E., Black, M. J. (No. 4), Max Planck Institute for Intelligent Systems, October 2012 pdf BibTeX
Thumb ticker lg coregtr
Perceiving Systems Technical Report Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material Freifeld, O., Black, M. J. (No. 5), Max Planck Institute for Intelligent Systems, October 2012 pdf BibTeX
Thumb ticker lg lietr
Perceiving Systems Technical Report MPI-Sintel Optical Flow Benchmark: Supplemental Material Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J. (No. 6), Max Planck Institute for Intelligent Systems, October 2012 pdf BibTeX
Thumb ticker lg sinteltr