Earticle

초록

영어

Constructing effective and generalizable synthesized motions is crucial for creating naturalistic, versatile, and effective virtual characters and robots. High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. I would like to go through several existing models such as Gaussian Process Dynamic Systems and Deep Belief Networks. I would analyze their strengths and limitations. I would also try to incorporate physical constraints to improve the motion quality. And on the other hand, try to improve the structure of the models or the learning algorithms.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Restricted Boltzmann Machine
 4. Gaussian Process Dynamical Model
  4.1. Gaussian Process Latent Variable Model
  4.2. Variational Inference
  4.3. Gaussian Process Dynamics
  4.4. Predictions
 5. Experiment
  5.1. Human Motion Caption Data
  5.2. Modeling Raw High Dimensional Video Sequences
 6. Performance Evaluation
 7. Conclusions
 Acknowledgment
 References

키워드

저자

• Zhou Zhi-Min [ Department of Computer Science Zhejiang University of Water Conservancy and Electric Power, Hangzhou, China ]
• Chen Zhong-Wen [ Department of Computer Science Zhejiang University of Water Conservancy and Electric Power, Hangzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605