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Year Published

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
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 Sheikh
September 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 Verbeek
August 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
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
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 Sheikh
August 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 Wolf
July 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
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 Ranzato
July 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