111 Results
September 8, 2018
DeepWrinkles: Accurate and Realistic Clothing Modeling
European Conference on Computer Vision (ECCV)
We present a novel method to generate accurate and realistic clothing deformation from real data capture. Previous methods for realistic cloth modeling mainly rely on intensive computation of physics-based simulation (with numerous heuristic parameters), while models reconstructed from visual observations typically suffer from lack of geometric details.
By: Zorah Lähner, Daniel Cremers, Tony Tung
AR/VR 
Computer Vision 
Facebook AI Research September 7, 2018
Recycle-GAN: Unsupervised Video Retargeting
European Conference on Computer Vision (ECCV)
We introduce a data-driven approach for unsupervised video retargeting that translates content from one domain to another while preserving the style native to a domain, i.e., if contents of John Oliver’s speech were to be transferred to Stephen Colbert, then the generated content/speech should be in Stephen Colbert’s style.
By: Aayush Bansal, Shugao Ma, Deva Ramanan, Yaser SheikhSeptember 7, 2018
Joint Future Semantic and Instance Segmentation Prediction
ECCV Anticipating Human Behavior Workshop
In this work, we introduce a novel prediction approach that encodes instance and semantic segmentation information in a single representation based on distance maps.
By: Camille Couprie, Pauline Luc, Jakob VerbeekAugust 20, 2018
TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering
Knowledge Discovery in Databases (KDD)
In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms.
By: Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, Jiawei Han
Natural Language Processing & Speech August 16, 2018
Constrained Bayesian Optimization with Noisy Experiments
Bayesian Analysis 2018
We derive an expression for expected improvement under greedy batch optimization with noisy observations and noisy constraints, and develop a quasi-Monte Carlo approximation that allows it to be efficiently optimized.
By: Ben Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
Data Science
Machine Learning August 14, 2018
Deep Appearance Models for Face Rendering
ACM SIGGRAPH
We introduce a deep appearance model for rendering the human face. Inspired by Active Appearance Models, we develop a data-driven rendering pipeline that learns a joint representation of facial geometry and appearance from a multiview capture setup.
By: Stephen Lombardi, Jason Saragih, Tomas Simon, Yaser SheikhAugust 13, 2018
Unsupervised Generation of Free-Form and Parameterized Avatars
TPAMI: SI: ICCV
We study two problems involving the task of mapping images between different domains. The first problem, transfers an image in one domain to an analog image in another domain. The second problem, extends the previous one by mapping an input image to a tied pair, consisting of a vector of parameters and an image that is created using a graphical engine from this vector of parameters.
By: Adam Polyak, Yaniv Taigman, Lior WolfJuly 29, 2018
Online Optical Marker-based Hand Tracking with Deep Labels
Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH)
We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks.
By: Shangchen Han, Beibei Liu, Robert Wang, Yuting Ye, Christopher D. Twigg, Kenrick Kin
Human Computer Interaction & UX July 13, 2018
Analyzing Uncertainty in Neural Machine Translation
International Conference on Machine Learning (ICML)
Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data.
By: Myle Ott, Michael Auli, David Grangier, Marc'Aurelio RanzatoJuly 13, 2018
A Multi-lingual Multi-task Architecture for Low-resource Sequence Labeling
Association for Computational Linguistics (ACL)
We propose a multi-lingual multi-task architecture to develop supervised models with a minimal amount of labeled data for sequence labeling.
By: Ying Lin, Shengqi Yang, Veselin Stoyanov, Heng Ji