NDLI: High Quality Binocular Facial Performance Capture from Partially Blurred Image Sequence

Content Provider	IEEE Xplore Digital Library
Author	Jian Jiang Ming Zeng Bojun Liang Xinguo Liu
Copyright Year	2013
Description	Author affiliation: Software Sch., Xiamen Univ., Xiamen, China (Ming Zeng) \|\| State Key Lab. of CAD&CG, Zhejiang Univ., Hangzhou, China (Jian Jiang; Bojun Liang; Xinguo Liu)
Abstract	Existing methods on passive facial performance capture assume that the input images are well captured. They merely consider how to deal with motion blurred images in the input sequence, which is very common in the image capture process. This paper presents a collection of novel algorithms and a thereby resulting system to reconstruct high quality facial dynamic geometry even from a partially blurred image sequence. In our method, we adopt binocular cameras to capture a stereo sequence. With this sequence, we first estimate depth map for each frame using a state-of-the-art stereo matching method. Then, based on the estimated depth map sequence, we track facial motion by leveraging constraints of both optical flow and geometry. In this step, a blur detection and regularization algorithm are devised to adaptively keep both shape and details. Finally, we synthesize temporal mesoscopic geometry on the blurred region from clear image texture of neighboring frames. We conduct extensive experiments on several sequences containing facial performance with blurred region, and the results demonstrate the effectiveness and robustness of our algorithms.
Starting Page	196
Ending Page	203
File Size	1399648
Page Count	8
File Format	PDF
ISBN	9781479925766
DOI	10.1109/CADGraphics.2013.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Geometry Tracking Shape facial performance capture temporal detail synthesis Optical imaging Cameras Face Image reconstruction motion blur
Content Type	Text
Resource Type	Article

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