This was facilitated by several papers that connected robust penalty functions to classical "line processes" used in Markov Random Fields (MRFs) at the time. Black and Rangarajan  characterized the formal properties of robust functions that have an equivalent line-process form and provided a process to convert between these formulations (known now as "Black-Rangarajan Duality".) Black and colleagues applied these ideas to image denoising, anisotropic diffusion, and principal component analysis (PCA)   The robust formulation was hand crafted and used small spatial neighborhoods. The work on Fields of Experts with Stefan Roth removed these restrictions. They learned the potential functions of an MRF with large spatial cliques by modeling the field potentials as a product of experts. Their formulation can be viewed as a shallow convolutional neural network. In 1993, Black and Jepson used mixture models to represent optical flow fields with multiple motions  (also called "layered" optical flow). This introduced the use of Expectation Maximization (EM) to the field of computer vision. In the 2000s, Black worked with John Donoghue and others at Brown University to create the technology behind the BrainGate neural prosthetics technology. Black and colleagues developed Bayesian methods to decode neural signals from motor cortex. The team was the first to use Kalman filtering  and particle filtering  to decode motor cortical ensemble activity. With these Bayesian decoding methods, the team demonstrated the successful point-and-click control of a computer cursor by a human with paralysis  and the decoding of full arm and hand movement in non-human primates. Black is best known for his work on human motion and shape estimation. With Hedvig Sidenbladh and David Fleet, he introduced the use of particle filtering for tracking 3D articulated human motion. This work was awarded the Koenderink Koenderink Prize for Fundamental Contributions in Computer Vision at ECCV 2000. His current work focuses on modeling and estimating human shape and pose from images and video. His team was the first to fit a learned 3D human body model to multi-camera image data at CVPR 2007, under clothing at ECCV 2008, from a single image at ICCV 2009, and from RGB-D data at ICCV 2011. His group produced the popular SMPL 3D body model  (and various extensions like FLAME  for 3D human faces, MANO  for 3D hands, and SMPL-X , an expresssive 3D body model with hands and faces) and popularized methods for estimating 3D body shape from images.