Browsing by Author "Chou, Yun-Feng"

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A nonparametric regression model for virtual humans generation
(2010-03-01)
Chou, Yun-Feng
;
Shih, Zen-Chung
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose a novel nonparametric regression model to generate virtual humans from still images for the applications of next generation environments (NG). This model automatically synthesizes deformed shapes of characters by using kernel regression with elliptic radial basis functions (ERBFs) and locally weighted regression (LOESS). Kernel regression with ERBFs is used for representing the deformed character shapes and creating lively animated talking faces. For preserving patterns within the shapes, LOESS is applied to fit the details with local control. The results show that our method effectively simulates plausible movements for character animation, including body movement simulation, novel views synthesis, and expressive facial animation synchronized with input speech. Therefore, the proposed model is especially suitable for intelligent multimedia applications in virtual humans generation.
Animating Characters Using Nonparametric Regression
(2008)
Chou, Yun-Feng
;
Shih, Zen-Chung
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose an image deformation technique using nonparametric regression to animate characters in a still image for multimedia applications. The proposed approach effectively produces a sequence of contiguous frames in an animation. It automatically generates deformed shapes by using elliptic radial basis functions (ERBFs) and locally weighted regression (LOESS). ERBFs are used for representing the deformed character's shapes in synthesized frames. For preserving the pattern within a shape, LOESS is applied to fit the detail with local control. Furthermore, the results show the synthesized frames without unnatural distortion.
Comic character animation using Bayesian estimation
(2011-09-01)
Chou, Yun-Feng
;
Shih, Zen-Chung
;
資訊工程學系
;
Department of Computer Science
The motion of comic characters includes different types of movements, such as walking or running. In a comic, a movement may be described by a series of non-continuous poses in a sequence of contiguous frames. Each pose exists in a frame. We synthesize an animation according to still comic frames. In this paper, we propose a model to analyze time series of a character's motion using the non-parametric Bayesian approach. Then we can automatically generate a sequence of motions by using the estimated time series. Experimental results show that the built time series model best matches the given frames. Furthermore, unnatural distortions of the results are minimized. Copyright (C) 2011 John Wiley & Sons, Ltd.