To React or not to React: End-to-End Visual Pose Forecasting for Personalized Avatar during Dyadic Conversations-Reference-Cited by-同舟云学术

To React or not to React: End-to-End Visual Pose Forecasting for Personalized Avatar during Dyadic Conversations

Published:2019-10-14 Issue: Volume: Page:
ISSN:
Container-title:2019 International Conference on Multimodal Interaction
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Author:

Ahuja Chaitanya¹,Ma Shugao²,Morency Louis-Philippe¹,Sheikh Yaser²

Affiliation:

1. Carnegie Mellon University, USA

2. Facebook Reality Labs, USA

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340555.3353725

Reference46 articles.

1. Task-based locomotion

2. Chaitanya Ahuja and Louis-Philippe Morency. 2018. Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling. In AAAI-18. 4996–5003. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17394 Chaitanya Ahuja and Louis-Philippe Morency. 2018. Lattice Recurrent Unit: Improving Convergence and Statistical Efficiency for Sequence Modeling. In AAAI-18. 4996–5003. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17394

3. 2D Human Pose Estimation: New Benchmark and State of the Art Analysis

4. Shaojie Bai J Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271(2018). Shaojie Bai J Zico Kolter and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271(2018).

5. The Effect of Behavioral Realism and Form Realism of Real-Time Avatar Faces on Verbal Disclosure, Nonverbal Disclosure, Emotion Recognition, and Copresence in Dyadic Interaction

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