GAMES Webinar 2018 -79期(Siggraph Asia 2018论文报告)| 曹晨(Snap Inc.),李旻辰(宾夕法尼亚大学)


【GAMES Webinar 2018-79期(Siggraph Asia 2018论文报告)】
报告嘉宾1:曹晨,Snap Inc.
报告时间:2018年12月27日 晚8:00-8:45(北京时间)
主持人:吕琳,山东大学(个人主页:http://irc.cs.sdu.edu.cn/~lulin/
报告题目: Stabilized Real-time Face Tracking via a Learned Dynamic Rigidity Prior
报告摘要:
Despite the popularity of real-time monocular face tracking systems in many successful applications, one overlooked problem with these systems is rigid instability. It occurs when the input facial motion can be explained by either head pose change or facial expression change, creating ambiguities that often lead to jittery and unstable rigid head poses under large expressions. Existing rigid stabilization methods either employ a heavy anatomically-motivated approach that are unsuitable for real-time applications, or utilize heuristic-based rules that can be problematic under certain expressions. We propose the first rigid stabilization method for real-time monocular face tracking using a dynamic rigidity prior learned from realistic datasets. The prior is defined on a region-based face model and provides dynamic region-based adaptivity for rigid pose optimization during real-time performance. We introduce an effective offline training scheme to learn the dynamic rigidity prior by optimizing the convergence of the rigid pose optimization to the ground-truth poses in the training data. Our real-time face tracking system is an optimization framework that alternates between rigid pose optimization and expression optimization. To ensure tracking accuracy, we combine both robust, drift-free facial landmarks and dense optical flow into the optimization objectives. We evaluate our system extensively against state-of-the-art monocular face tracking systems and achieve significant improvement in tracking accuracy on the high-quality face tracking benchmark. Our system can improve facial-performance-based applications such as facial animation retargeting and virtual face makeup with accurate expression and stable pose. We further validate the dynamic rigidity prior by comparing it against other variants on the tracking accuracy.
讲者简介:
Chen Cao is now a research scientist at Snap Inc. His research interest is human digitalization, especially on 3D face modeling, realtime 3D face tracking, digital avatar generation and animation etc. In the past years, he has published 5 SIGGRAPH/SIGGRAPH Asia paper, 1 TVCG paper in the related fields. Before joining Snap Inc., he obtained the PhD from Zhejiang University, supervised by Prof. Kun Zhou.
讲者个人主页:https://sites.google.com/site/zjucaochen/home

 

报告嘉宾2:李旻辰,宾夕法尼亚大学
报告时间:2018年12月27日 晚8:45-9:30(北京时间)
主持人:吕琳,山东大学(个人主页:http://irc.cs.sdu.edu.cn/~lulin/
报告题目:OptCuts: Joint Optimization of Surface Cuts and Parameterization
报告摘要:
Low-distortion mapping of three-dimensional surfaces to the plane is a critical problem in geometry processing. The intrinsic distortion introduced by these UV mappings is highly dependent on the choice of surface cuts that form seamlines which break mapping continuity. Parameterization applications typically require UV maps with an application-specific upper bound on distortion to avoid mapping artifacts; at the same time they seek to reduce cut lengths to minimize discontinuity artifacts. We propose OptCuts, an algorithm that jointly optimizes the parameterization and cutting of a three-dimensional mesh. OptCuts starts from an arbitrary initial embedding and a user-requested distortion bound. It requires no parameter setting and automatically seeks to minimize seam lengths subject to satisfying the distortion bound of the mapping computed using these seams. OptCuts alternates between topology and geometry update steps that consistently decrease distortion and seam length, producing a UV map with compact boundaries that strictly satisfies the distortion bound. OptCuts automatically produces high-quality, globally bijective UV maps without user intervention. While OptCuts can thus be a highly effective tool to create new mappings from scratch, we also show how it can be employed to improve pre-existing embeddings. Additionally, when semantic or other priors on seam placement are desired, OptCuts can be extended to respect these user preferences as constraints during optimization of the parameterization. We demonstrate the scalable performance of OptCuts on a wide range of challenging benchmark parameterization examples, as well as in comparisons with state-of-the-art UV methods and commercial tools.
讲者简介:
李旻辰,宾夕法尼亚大学计算机与信息科学博士在读,师从蒋陈凡夫教授,主要研究优化问题和数值模拟在几何处理与计算机动画中的应用。他2015年本科毕业于浙江大学竺可桢学院混合班,主修计算机科学与技术,并于2018年获得英属哥伦比亚大学计算机科学硕士,曾两次在Adobe研究院参与科研实习。
讲者个人主页:http://www.seas.upenn.edu/~minchenl/

 

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