Mirror MoCap: Automatic and efficient capture of dense 3D facial motion parameters from video

Lin, I-Chen; Ouhyoung, Ming

doi:10.1007/s00371-005-0291-5

Cited by 27 publications

(14 citation statements)

References 28 publications

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“…We also used the algorithm proposed by Lin and Ouhyoung [1], which employs the weighted sum of the displacements of more than three neighbors, and we designed a new formula to generate the weights. Our method uses the muscle motion mechanism for three neighbors to recover missing data.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…To address this problem, we applied three separate recovery methods to our captured motion data. To validate our method's validity, we reconstructed facial motion expressions using Guenter's [2] algorithm with three neighbors, and we also conducted facial reconstruction with Lin and Ouhyoung's [1] method based on the weighted summation of the displacements neighbors to recover missing data. Finally, we complete the facial reconstruction process using our method based on the muscle motion mechanism with three neighbors.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…However, 3D geometric models can not be driven directly by the source motion data captured by passive optical Mo-Cap systems. The major reasons for this are as follows: (1) there is unavoidable noise in the captured source data, which comes mainly from calibration errors, electrical noise, and jumping by sensor devices or markers; and (2) there are missing data. During the processing of captured motion data, multiple markers often appear to overlap or obscure each other, or they may be invisible compared with the situation when using two cameras, all of which makes the positions of these markers difficult to reconstruct and missing data are the outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, many effective methods have been proposed for missing data recovery. Lin and Ouhyoung [1] proposed the weighted summation of neighbor displacements to repair missing data, where the distance between neighboring markers and missing markers determined the weights of the movements of neighboring markers, so the weights became smaller as the distance increased. Guenter et al [2] used the average displacements of valid neighbors to represent the movements of missing data, which repaired the missing data.…”

Section: Introductionmentioning

confidence: 99%

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Scattered Data Processing Approach Based on Optical Facial Motion Capture

Zhang

Liang

Wei

2013

Applied Bionics and Biomechanics

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Abstract. In recent years, animation reconstruction of facial expressions has become a popular research field in computer science and motion capture-based facial expression reconstruction is now emerging in this field. Based on the facial motion data obtained using a passive optical motion capture system, we propose a scattered data processing approach, which aims to solve the common problems of missing data and noise. To recover missing data, given the nonlinear relationships among neighbors with the current missing marker, we propose an improved version of a previous method, where we use the motion of three muscles rather than one to recover the missing data. To reduce the noise, we initially apply preprocessing to eliminate impulsive noise, before our proposed three-order quasi-uniform B-spline-based fitting method is used to reduce the remaining noise. Our experiments showed that the principles that underlie this method are simple and straightforward, and it delivered acceptable precision during reconstruction.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scattered Data Processing Approach Based on Optical Facial Motion Capture

Zhang

Liang

Wei

2013

Applied Bionics and Biomechanics

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“…Lin et al 10,11 proposed capturing dense facial markers with mirror-reflected views. Even though they tracked nearly three hundred markers, they still had difficulty in reconstructing wrinkles or dimples.…”

Section: Related Workmentioning

confidence: 99%

Image‐based detail reconstruction of non‐Lambertian surfaces

Lin

Chang²,

Lo³

et al. 2010

Computer Animation & Virtual

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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 textureless 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.

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