liu yang umass


We present RigNet, an end-to-end automated method for producing animation rigs from input character models. We apply our tube-and-droplet representation to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition. Email: [email protected] / ... Liu Yang, Minghui Qiu, Swapna Gottipati, Feida Zhu, Jing Jiang, Huiping Sun and Zhong Chen. Proposed Menu.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan. Much research on response selection in conversation systems is modeling the matching patterns between user input message (either with context or not) and response candidates, which ignores external knowledge beyond the dialog utterances. This kernel-based estimation effectively leverages both multiple structural information within a trajectory and the local motion patterns across multiple trajectories, such that the discrimination of the shrunk point can be properly increased. 2018] Our paper VisemeNet accepted by SIGGRAPH 2018. We also study how to integrate user intent modeling into neural ranking models to improve response retrieval performance. Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

[Apr. Collaborate with researchers on audio-driven cartoon and real human facial animations and lip-sync technologies based on deep learning approaches. Sep. 2020: talk at UMass CICS Computing and Social Justice series ; Sep. 2020: talk at UPenn CLunch; Jul. [Sept. 2020] I'm looking for job/post-doc positions (starting from around May 2021)! 2018] Joined Wayfair Next Research as a summer intern and fall co-op intern. [Project Page] Copyright, Artificial Intelligence and Robotics Commons, Databases and Information Systems Commons.

Prof. J. Joshua Yang and his research group focus on Post-CMOS ionic and electronic devices, with applications in unconventional computing technologies. Our goal is to develop effective learning models for answer retrieval and information-seeking conversations, in order to improve the effectiveness and user experience when accessing information with a touch screen interface or a conversational interface, as commonly adopted by millions of mobile Internet devices.

On the other hand, voice-based / text-based conversational interfaces are becoming increasing popular as shown in the wide adoption of intelligent assistant services and devices such as Amazon Echo, Microsoft Cortana and Google Assistant around the world. The audio content robustly controls the motion of lips and nearby facial regions, while the speaker information determines the specifics of facial expressions and the rest of the talking head dynamics.

Collaborate with researchers on 3D facial/skeleton animations based on deep learning approaches.

2019] Joined Adobe CIL (Seattle) as a summer intern. Finally, hybrid models of response retrieval and generation are investigated in order to combine the merits of these two different paradigms of conversation models. ShapeNet is an ongoing effort to establish a richly-annotated, large-scale dataset of 3D shapes.

Artificial Intelligence and Robotics Commons, Author.

ETDS We propose a learning framework on top of deep neural matching networks that leverages external knowledge with pseudo-relevance feedback and QA correspondence knowledge distillation for response retrieval.

[Video]. We introduce an adaptive multi-kernel-based estimation process to estimate the 'shrunk' positions and speeds of trajectories' points. [Project Page] Databases and Information Systems Commons.

In particular, we help check the geometry duplicates in ShapeNet Core dataset. Title.

We start from the research on single-turn answer retrieval and analyze the weaknesses of existing deep learning architectures for answer ranking.

In Proceedings of the 22nd ACM International Conference on Information and Knowledge Management , San Francisco, CA, USA.

[Paper] Full Oral Paper, Acceptance rate=16.8% (143 … Our distribution is predicted though passing learned messages in a dense graph whose nodes represent objects in the input scene and edges represent spatial and structural relationships. These important changes have triggered several new challenges that search engines have had to adapt to in order to better satisfy the information needs of mobile Internet users. Sign in|Recent Site Activity|Report Abuse|Print Page|Powered By Google Sites, Top 10 Undergraduates of Liaoning Province, Department of Education of Liaoning Province, 2011, Outstanding Bachelor Graduate of Liaoning Province(top 2%), Department of Education of Liaoning Province, 2011, ACM-ICPC Programming Contest ,second prize, Northeastern University, 2011, International Mathematical Contest in Modelling(MCM) , Honorable Mention, SIAM and MAA, 2010, Excellent Academic Award, Northeastern University, 2010, Suzhou Industrial Park Scholarship(top 2%), Suzhou Industrial Park, 2009, Chinese Undergraduate Mathematical Contest in Modeling(CUMCM) ,National Second Prize , 2009, Outstanding Student Scholarship, First Grade(top 3%), Northeastern University, 2008-2011, Center for Intelligent Information Retrieval (CIIR), ShellMiner: Mining Organizational Phrases in Argumentative Texts in Social Media, Generating Supplementary Travel Guides from Social Media, CQARank: Jointly Model Topics and Expertise in Community Question Answering, Modeling Interaction Features for Debate Side Clustering, Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization, An Integrated Model for User Attribute Discovery: A Case Study on Political Affiliation Identification, PredictingUser's Political Party using Ideological Stances, Center for Intelligent Information Retrieval(CIIR), Center for Intelligent Information Retrieval (CIIR), UMass, Living Analytics Research Centre, SMU/CMU, Best Paper Award Runner-ups of SocInfo 2013, IBM Chinese Excellent Student Scholarship.

We present a learning method for predicting animation skeletons for input 3D models of articulated characters.

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In this dissertation, we investigate several aspects of single-turn answer retrieval and multi-turn information-seeking conversations to handle the new challenges of search on the mobile Internet. In contrast to previous approaches that fit pre-defined skeleton templates or predict fixed sets of joints, our method produces an animation skeleton tailored for the structure and geometry of the input 3D model. Doctoral Dissertations. [Aug. 2020] Our paper MakeItTalk conditioally accepted by SIGGRAPH ASIA 2020. [Project Page]

► [Apr.

► [Jul. We address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. © About. We present a novel deep-learning based approach to producing animator-centric speech motion curves that drive a JALI or standard FACS-based production face-rig, directly from input audio.

Visit; Apply; Give; Search UMass.edu; College of Natural Sciences Department of Chemistry. Working on mobile game design, especially on profit models and user-experiences. > [Paper]. ► [NEW!] 1781.

[Youtube Link] [Press].

2019] Our paper SceneGraphNet accepted by ICCV 2019. We propose a 3D tube which can effectively embed both motion and scene-related information of a motion trajectory and a droplet-based method which can suitably catch the characteristics of the 3D tube for activity recognition. The increasing popularity of mobile Internet has led to several crucial changes in the way that people use search engines compared with traditional Web search on desktops. © 2009 University of Massachusetts Amherst • Site Policies, Privacy [New Video!]


Given an input, potentially incomplete, 3D scene and a query location, our method predicts a probability distribution over object types that fit well in that location. It also estimates surface skin weights based on the predicted skeleton.

Check our demo video [here], ► [NEW!] October 2013.

Liu Yang, College of Information and Computer Sciences, UMass Amherst Follow. UMass Amherst user name and password. [Paper] Based on this intermediate representation, our method is able to synthesize photorealistic videos of entire talking heads with full range of motion and also animate artistic paintings, sketches, 2D cartoon characters, Japanese mangas, stylized caricatures in a single unified framework. [Paper] 2020] Our paper RigNet accepted by SIGGRAPH 2020. [Code]

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I'm Yang Zhou I'm a 5th year CS PhD student in the Computer Graphics Research Group at UMass Amherst, advised by Prof. Evangelos Kalogerakis. [3D Shape Reconstruction and Segmentation Task Page] [Project Page]
1781.

Response Retrieval in Information-seeking Conversations, Liu Yang, College of Information and Computer Sciences, UMass AmherstFollow, Artificial Intelligence and Robotics | Databases and Information Systems.

Response Retrieval in Information-seeking Conversations. ► [Jun. please use the following link to log into our proxy server with your ► [Aug. 2019] Our paper on Animation Skeleton Prediction accepted by 3DV 2019. Yang, Liu, "Response Retrieval in Information-seeking Conversations" (2019). [Code]. This work is licensed under a Creative Commons Attribution 4.0 License. CQARank: Jointly Model Topics and Expertise in Community Question Answering. Mar.

[Project Page] Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan. [Video].

Our proposed model achieves state-of-the-art performance for answer sentence retrieval compared with both feature engineering based methods and other neural models.

We present a method that generates expressive talking heads from a single facial image with audio as the only input. 2020: talk at Data Science fwdays'20; May 2020: ACL 2020 paper on "stupid" attention mechanisms now available! [Paper] ► [NEW!]

Copyright ©document.write(new Date().getFullYear()); All rights reserved | This template is made by Colorlib. Our method is based on a deep architecture that directly operates on the mesh representation without making assumptions on shape class and structure. Mobile Internet users prefer direct answers on the search engine result page (SERP).

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[Code]. Then we propose an attention based neural matching model with a value-shared weighting scheme and attention mechanism to improve existing deep neural answer ranking models. I work in the areas of computer graphics and machine learning. On one hand, there is limited output bandwidth with the small screen sizes of most mobile devices.

[Video], ► [Nov. 2019] Our summer intern project #SweetTalk was presented at Adobe MAX 2019 (Sneak Peek).

Home [Video] [Code]

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October 27, 2020

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