Browsing by Author "Peng, Jen-Yu"

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Adaptive Motion Data Representation with Repeated Motion Analysis
(2011-04-01)
Lin, I-Chen
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Peng, Jen-Yu
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Lin, Chao-Chih
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Tsai, Ming-Han
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資訊工程學系
In this paper, we present a representation method for motion capture data by exploiting the nearly repeated characteristics and spatiotemporal coherence in human motion. We extract similar motion clips of variable lengths or speeds across the database. Since the coding costs between these matched clips are small, we propose the repeated motion analysis to extract the referred and repeated clip pairs with maximum compression gains. For further utilization of motion coherence, we approximate the subspace-projected clip motions or residuals by interpolated functions with range-aware adaptive quantization. Our experiments demonstrate that the proposed feature-aware method is of high computational efficiency. Furthermore, it also provides substantial compression gains with comparable reconstruction and perceptual errors.
Image-based detail reconstruction of non-Lambertian surfaces
(2010-01-01)
Lin, I-Chen
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Chang, Wen-Hsing
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Lo, Yung-Sheng
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Peng, Jen-Yu
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Lin, Chan-Yu
This paper presents a novel optimization framework for estimating the static or dynamic surfaces with details. The proposed method uses dense depths from a structured-light system or sparse ones from motion capture as the initial positions, and exploits non-Lambertian reflectance models to approximate surface reflectance. Multi-stage shape-from-shading (SFS) is then applied to optimize both shape geometry and reflectance properties. Because this method uses non-Lambertian properties, it can compensate for triangulation reconstruction errors caused by view-dependent reflections. This approach can also estimate detailed undulations on text-tireless regions, and employs spatial-temporal constraints for reliably tracking time-varying surfaces. Experiment results demonstrate that accurate and detailed 3D surfaces can be reconstructed from images acquired by off-the-shelf devices. Copyright (C) 2010 John Wiley & Sons, Ltd.
Interactive and flexible motion transition
(2007-09-01)
Peng, Jen-Yu
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Lin, I-Chen
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Chao, Jui-Hsiang
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Chen, Yan-Ju
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Juang, Gwo-Hao
In this paper, we present an example-based motion synthesis technique. Users can interactively control the virtual character to perform desired actions in any order. The desired action can be not only recorded or pre-computed motion, but also parametric synthesized one to attain the precise control of avatars. Moreover, a user can change their commands any time to switch to another action according to the instant response of opponents infighting. The quality transition motions between consecutive actions are rapidly synthesized through traversing a simple graph structure which represents the transition relationships between different poses. The graph is constructed according to clustering on frames in a corpus of motion capture data. With the pre-computation of path finding, our approach can also be applied to real-time applications. Besides, this pre-computed graph structure can be used to transit those motions not included in the database. Furthermore, our approach is automatic without any human intervention. The final results demonstrate the potential of our algorithm. Copyright (c) 2007 John Wiley & Sons, Ltd.
Partial Motion Blending and Assembly for Interactive Motion Synthesis
(2008)
Juang, Gwo-Hao
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Chen, Yan-Ju
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Peng, Jen-Yu
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Lin, I-Chen
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Chao, Jui-Hsiang
Interactively controlling or editing motion capture data is an intriguing topic in games or animation prototyping. However, data-driven approaches require a large data set to synthesize motion of high variety. In the first part of this paper, we propose novel partial motion synthesis to extend the control parameter space or variety of limited motion data. We extend the attack range of kicking or punching by blending body parts respectively and then reassembling. Users can simply assign a target position in the extended parameter space and will get a motion that hit the desired target. In the second application, we propose applying partial motion assembly for motion editing. Given a sequence of key postures, our system will retrieve resembling partial figures from data sets. While reassembling the different parts of character motion and adjusting the motion variance according to the query motion, our novel synthetic motions can preserve original style and naturalness.