1. Local Motion Phases: Local Motion Phases for Learning Multi-Contact Character Movements. 36, 4, Article 42. Graph. 2015. Daniel Holden, Taku Komura, Jun Saito. 36, 4, Article 42. Google Scholar Digital Library; Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce. Aden Young (born 30 November 1971) is a Canadian-Australian actor.He is best known for his portrayal of Daniel Holden in the SundanceTV drama Rectify, for which he was twice nominated for the Critics' Choice Television Award for Best Actor in a Drama Series. Daniel Holden, Taku Komura, Jun Saito. 36, 4, Article 42. Graph. Daniel Holden, a researcher at Ubisoft Montreal and lead researcher on this project, described PFNN as: A learning framework that is suitable for generating cyclic behavior such as human locomotion. Daniel Holden must put his life back together after serving 19 years on Georgia's Death Row before DNA evidence calls his conviction into question. Sebastian Starke, Yiwei Zhao, Taku Komura, Kazi Zaman. PFNN: Phase-Functioned Neural Networks for Character Control. In … A deep learning framework for character motion synthesis and editing. This work continues the recent work on PFNN (Phase-Functioned Neural Networks) for character control. Graph. Daniel Holden We present a real-time character control mechanism using a novel neural network architecture called a Phase-Functioned Neural Network. DANIEL HOLDEN, University of Edinburgh TAKU KOMURA, University of Edinburgh JUN SAITO, Method Studios Fig. 2016. With Aden Young, Abigail Spencer, J. Smith-Cameron, Adelaide Clemens. SIGGRAPH 2020. ”Daniel Holden解释称，“然后我们用机器学习技术制作一个系统，它可以直接将用户的输入映射到动画角色上。 这样一来，我们就没有必要存储所有数据，从中选择相应的片断，我们有了一套系统，它可以快速生成动画，根据用户的输入信息生成。 ACM Trans on Graph 35, 4 (2016). Rectify is an American television drama series exploring the life of a man after he is released from prison after nearly 20 years on death row following a wrongful conviction. 爱丁堡大学 Daniel Holden，Taku Komura 和 Method Studios 的 Jun Saito 三位研究者提出“用于角色控制的 Phase-Functioned 神经网络”（Phase-Functioned Neural Networks for Character Control，简称 PFNN），是处理角色动画的全新方法，将在今年夏天的 ACM SIGGRAPH 大会上展示。 In SIGGRAPH Asia 2015 Technical Briefs. Learning Motion Manifolds with Convolutional Autoencoders. ACM Trans. ACM Trans. Daniel Holden, Jun Saito, and Taku Komura. Daniel Holden, Taku Komura, Jun Saito. Created by Ray McKinnon. SIGGRAPH 2017. ACM Trans.
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