Search Results for author: Iain Matthews

Found 10 papers, 2 papers with code

Personalized 3D Human Pose and Shape Refinement

no code implementations18 Mar 2024 Tom Wehrbein, Bodo Rosenhahn, Iain Matthews, Carsten Stoll

To address this issue, we propose to construct dense correspondences between initial human model estimates and the corresponding images that can be used to refine the initial predictions.

3D human pose and shape estimation

Speech Driven Tongue Animation

no code implementations CVPR 2022 Salvador Medina, Denis Tome, Carsten Stoll, Mark Tiede, Kevin Munhall, Alexander G. Hauptmann, Iain Matthews

In this work, we introduce a large-scale speech and mocap dataset that focuses on capturing tongue, jaw, and lip motion.

Learning Physics-guided Face Relighting under Directional Light

no code implementations CVPR 2020 Thomas Nestmeyer, Jean-François Lalonde, Iain Matthews, Andreas M. Lehrmann

Relighting is an essential step in realistically transferring objects from a captured image into another environment.

Factorized Variational Autoencoders for Modeling Audience Reactions to Movies

no code implementations CVPR 2017 Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori

Matrix and tensor factorization methods are often used for finding underlying low-dimensional patterns from noisy data.

Panoptic Studio: A Massively Multiview System for Social Interaction Capture

1 code implementation9 Dec 2016 Hanbyul Joo, Tomas Simon, Xulong Li, Hao liu, Lei Tan, Lin Gui, Sean Banerjee, Timothy Godisart, Bart Nabbe, Iain Matthews, Takeo Kanade, Shohei Nobuhara, Yaser Sheikh

The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions.

Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines

no code implementations NeurIPS 2011 Matthew D. Zeiler, Graham W. Taylor, Leonid Sigal, Iain Matthews, Rob Fergus

We present a type of Temporal Restricted Boltzmann Machine that defines a probability distribution over an output sequence conditional on an input sequence.

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