從單張人臉影像中估測三維外形、表情變化、照?與光源

三維人臉模型在許多的應用中都是相當熱門的主題，例如人臉動畫、人臉辨識以及虛擬人物會議等的應用。所以如何正確的模型化臉部的幾何、材質顏色以及光源變化在電腦視覺以及圖學都漸形重要。在文獻中從單張影像模型化三維人臉主要利用了事前訓練的資訊。然而，要在有包含人臉表情變化的單張影像中正確地重建出人臉幾何仍然相當困難，因為表情變化使得三維空間中臉部表面的變形更為複雜。主要的挑戰在於必需同時分析無表情的原始臉部幾何，以及其附加的表情變化，這使得問題更為棘手。另一方面，光源的變化，常常因為臉部材質以及角度而有所不同，困難度更為提高。在此篇論文中，我們主要發展了一套完整的系統，能夠重建三維人臉幾何、表情、照度、光源等的資訊。系統包含了訓練的步驟，也就是如何自動地在人臉模型間建立點與點的對應關係，以及描述無情形幾何、表情變化的模型化方法。在決定三維表面的對應關係時，我們是將三維的模型先參數化到二維的平面上，如此問題就變成二維平面上點對應問題。利用線性的主成分分析表示人臉原始外形，而使用非線性的流行分析描述表形變化。我們也同時提出三維人臉重建的演算法，基於訓練階段的模型，我們結合了線性與非線性子空間的表示法來描述無表情人臉形狀幾何與機率化流形三維表情變化。在此系統中，我們整合了人臉幾何、表情變化、材質與光源種種的資訊，如此在解決此問題時有良好的限制而得到合理的結果。實驗中的驗證支持我們所發展的系統，而所產生的結果也能夠延伸並適用於不同的應用，例如影像合成，光源估測，表情移除及表情轉換，以及人臉特徵誇張化等。

關鍵字

三維重建；人臉重建；流形學習

並列摘要

Three-dimensional human face modeling is a very popular topic with many applications,such as facial animation, face recognition and model-based facial video communication. Therefore, how to model the facial geometry, texture intensity and illumination variation is important in computer vision and graphics. Previous works on 3D head modeling from a single face image utilized prior information on 3D head models. However, it is difficult to accurately reconstruct the 3D face model from a single face image with expression since the facial expression induces 3D face model deformation in a complex manner. The main challenge is the coupling of the neutral 3D face model and the 3D deformation due to expression, thus making the 3D model estimation from a single face image with expression very challenging. On the other hand, the illumination condition also makes the problem more difficult. In this thesis,we focus on developing a 3D face model reconstruction system including surface registration and training of 3D face models with expressional deformations as well as the estimation of the 3D neutral shape and the 3D expressional deformation from a single face image. The proposed reconstruction algorithm integrates the linear and non-linear subspace representations for a prior 3D neutral morphable model and the probabilistic manifold-based 3D expressional deformation. We incorporate the face geometry, expression deformation, texture and illumination information into the problem so that it is well constrained. The reconstructed 3D face models can also be further extended and applied to many real-world applications.

並列關鍵字

3D Reconstruction ； Face Reconstruction ； Manifold Learning

參考文獻

[2] T. Sim, S. Baker, and M. Bsat, “The cmu pose, illumination, and expression database,” IEEE

[3] V. Blanz and T. Vetter, “A morphable model for the synthesis of 3d-faces,” in ACM Transactions

[4] V. Blanz and T. Vetter, “Face recognition based on fitting a 3d morphable model,” IEEE

Transaction on Pattern Analysis and Machine Intelligent, vol. 25, no. 9, pp. 1063–1074, 2003.

[5] J. Davis, R. Ramamoorthi, and S. Rusinkiewicz, “Spacetime stereo: A unifying framework for depth

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從單張人臉影像中估測三維外形、表情變化、照?與光源

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