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Capturing Facial Details by Space- time Shape-from-shading Yung-Sheng Lo *, I-Chen Lin *, Wen-Xing Zhang *, Wen-Chih Tai †, Shian-Jun Chiou † CAIG Lab, Dept. of CS, National Chiao Tung University * Chunghwa Picture Tubes, LTD. † 1
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Outline Introduction Acquisition of facial motion Space-time shape-from-shading Experiment and results Conclusion 2
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Introduction Performance-driven method is one of the most straightforward method for facial animation. Expression details, e.g. wrinkles, dimples, are key factors but difficult for motion capture. Original captured imagesDeformation without details 3
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Introduction (cont.) Physics-based simulation and blend shape methods try to mimic the details. But, the synthesized details are not the exact expressions. Muscle-based [E. Sifakis et al. 2005] Blend shape [Z. Deng et al. 2006] 4
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Introduction (cont.) Our goal is to enhance existing motion capture tech. and capture facial details. SFS With the captured images and directional lighting, our optimization-based shape-from-shading (SFS) can estimate details from shading in video. With facial details Captured video 5
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The proposed method Combines the benefits of motion capture and shape-from-shading. Motion capture and stereo reconstruction general geometry accurate on feature points and general geometry. Unreliable corresponding matching at textureless regions Shape-from-shading don’t needcorrespondencefor textureless regions don’t need detailed point correspondence for textureless regions. relative undulation Estimate relative undulation. Sensitive to noise. Space-time shape-from-shading Motion capture + Space-time shape-from-shading. 6
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The proposed method 7
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Approximate geometry by Mocap Tracking by block matching and stereo reconstruction. Deforming a generic face model by radial-basis functions (RBF). 8
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Facial details by SFS Estimating time-varying details by iterative approximating shape V and reflectance R. Input image 9
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Space-time constraints Only SFS is not enough. For more reliable detailed motions, we proposed using space-time constraints. 10 Highly sensitive to noise After applying our spatial constraints
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Spatial constraints Mostly continuous surface High spatial coherence 11 ∆ For J Є Neighbor(p) ∆ Neighbor(p) denotes the 8-neighbor pixel set ∆ Wj is an adaptive weight ∆ Kcs is the weight for spatial constraints. Reduce the noise noise Ztp Ztj Ztp Ztj noise Ztp Ztj
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Still flicker According to biomechanics properties: A human facial surface should gradually transit between expressions. Temporal constraints 12 T0T1 flicker T2 T0 A video image sequence
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Space-time shape-from-shading Finally, our objective function becomes 13 spatial constraints + temporal constraints + shading constraints = Space-time shape-from-shading
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Performance issue if applied our optimization to the whole face. DOF is too large Assigned some small windows. We preferred areas with more wrinkles and creases. 14 D.O.F=N*M (pixels) *i(frames) N M Video image sequence Fi … F2 F1 F3
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Experiment Illumination-controlled (single light source) Two video streams. (HDV, 1280*720,30 fps) We pasted 25 to 30 markers on human face. 15 { C1 } {C2 }
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Facial detailed results and Comparison 16
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Result of synthesis Generic model: 6078 vertices 6315 polygons 17 deformation (RBF) subdivision per-pixel normal mapping
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Result of animation 18
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Conclusion & Future work We propose capturing detailed motion by conventional Mocap and advanced shape-from- shading. Doesn’t need additional devices, paint pigments, or restrict the wrinkle shape. With spatial and temporal constraints, our optimal shape-from-shading is more reliable. Reflectance parameters are also estimated. 19
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Conclusion & Future work In addition to Phong model, we will extend the concept to other reflectance models. E.g Cook-Torrance BRDF model, BSSRDF, etc) Currently, SFS is only applied to designated segments. An more efficient SFS for the whole face will make our animation more realistic. 20
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Thank for your attention! 21 forehead details between eyebrows smile
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