Text to Animation Reading Group

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The goal of the Text to Animation project is to connect natural language processing and graphics. The current focus of the project is on generating human motion animations from natural language descriptions. As a result, we read papers in the following topic areas: sequence-to-sequence modeling, motion modeling, text-to-pixels, and motion controller learning for animated characters.

Spring 2018 Meeting Time & Place

• Biweekly meetings on Mondays, 1 p.m. - 2 p.m. in GDC 3.816

Scheduled Meetings

Date	Time	Place	Paper
Meetings will resume in Fall 2018.

* Future meetings subject to rearrangement.

* Please send in suggestions for new papers to discuss, or vote for a paper from the currently proposed papers.

Past Meetings

Date	Time	Place	Paper
05/07/18	1PM	GDC 3.816	Xue Bin Peng, Pieter Abbeel, Sergey Levine, Michiel van de Panne DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills, ACM Transactions on Graphics, 2018 Project website (Includes videos)
04/23/18	1PM	GDC 3.816	Daniel Holden, Taku Komura, and Jun Saito Phase-Functioned Neural Networks for Character Control, ACM Transactions on Graphics (TOG), 2017 Project website (includes videos)
04/09/18	1PM	GDC 3.816	Yitong Li, Martin Renqiang Min, Dinghan Shen, David Carlson, and Lawrence Carin Video Generation from Text, Proc. American Association of Artificial Intelligence (AAAI), 2018
03/26/18	1PM	GDC 3.816	Ziyu Wang*, Josh Merel*, Scott Reed, Greg Wayne, Nando de Freitas, Nicolas Heess (* Joint First Authors) Robust Imitation of Diverse Behaviors, NIPS, 2017 Project website (Supplemental material includes videos)
03/05/18	1PM	GDC 3.816	Partha Ghosh, Jie Song, Emre Aksan, and Otmar Hilliges Learning Human Motion Models for Long-term Predictions, International Conference on 3D Vision, 2017 (Received Best Paper Award) Project website (Includes videos)
02/19/18	1PM	GDC 3.816	Zimo Li, Yi Zhou, Shuangjiu Xiao, Chong He, and Hao Li Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis, arXiv, 2018
02/05/18	1PM	GDC 3.816	Junyoung Chung, Sungjin Ahn, and Yoshua Bengio Hierarchical Multiscale Recurrent Neural Networks, ICLR, 2017
01/22/18	1PM	GDC 3.816	Xue Bin Peng, Glen Berseth, Kangkang Yin, and Michiel van de Panne DeepLoco: Dynamic Locomotion Skills Using Hierarchical Deep Reinforcement Learning, ACM Transactions on Graphics, 2017 Project website (Includes videos)
01/10/18	11AM	GDC 5.816	Julieta Martinez, Michael J. Black, and Javier Romero On human motion prediction using recurrent neural networks, CVPR, 2017
12/11/17	12PM	GDC 3.816	Ikhsanul Habibie, Daniel Holden, Jonathan Schwarz, Joe Yearsley, and Taku Komura A Recurrent Variational Autoencoder for Human Motion Synthesis, British Machine Vision Conference, 2017
12/04/17	2PM	GDC 5.816	Daniel Holden, Jun Saito, and Taku Komura A Deep Learning Framework for Character Motion Synthesis and Editing, SIGGRAPH, 2016
11/13/17	2PM	GDC 5.816	Yang Li, Nan Du, and Sammy Bengio Time-Dependent Representation for Neural Event Sequence Prediction, arXiv
10/30/17	2PM	GDC 5.816	Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess Learning Human Behaviors from Motion Capture by Adversarial Imitation, arXiv
10/16/17	2PM	GDC 5.816	Han Zhang, Tao Xu, Hongshen Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris Metaxas StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, ICCV, 2017
10/02/17	2PM	GDC 5.816	Prajit Ramachandran, Peter J. Liu, and Quoc V. Le Unsupervised Pretraining for Sequence to Sequence Learning, ACL, 2017
09/18/17	2PM	GDC 5.816	Cihan Halit and Tolga Capin Multiscale motion saliency for keyframe extraction from motion capture sequences, Computer Animation and Virtual Worlds Journal, January 2011
08/29/17	2PM	GDC 5.816	Matthias Plappert, Christian Mandery, and Tamim Asfour Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks, arXiv, 2017