Fitting a Morphable Model to 3D Scans of Faces

Blanz, Volker; Scherbaum, Kristina; Seidel, Hans‐Peter

doi:10.1109/iccv.2007.4409029

Cited by 72 publications

(52 citation statements)

References 28 publications

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“…This VR is quite good if consider that this result can be achieved in less than 0.1 msec per match, while the computational time of fitting a model is on the order of minutes [12,3] in a powerful desktop.…”

Section: Frgc V2mentioning

confidence: 96%

“…We conducted two experiments. In the first one, we fitted an annotated pre-segmented model in the data [3]. In the second one, we simply aligned the data using the eye coordinates provided by the meta-data of FRGC.…”

Section: Frgc V2mentioning

confidence: 99%

“…The major drawback of these methods is the high computational complexity related to the computation of geodesics. Another solution is to an annotated morphable model [12,14,3]. One of the drawbacks of these methods is that performance depends on the success of the fitting algorithm which often fails in cases of large expressions or occlusions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Learning from Normals for 3D Face Recognition

Marras

Zafeiriou

Tzimiropoulos

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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Abstract. We introduce novel subspace-based methods for learning from the azimuth angle of surface normals for 3D face recognition. We show that the normal azimuth angles combined with Principal Component Analysis (PCA) using a cosine-based distance measure can be used for robust face recognition from facial surfaces. The proposed algorithms are well-suited for all types of 3D facial data including data produced by range cameras (depth images), photometric stereo (PS) and shade-from-X (SfX) algorithms. We demonstrate the robustness of the proposed algorithms both in 3D face reconstruction from synthetically occluded samples, as well as, in face recognition using the FRGC v2 3D face database and the recently collected Photoface database where the proposed method achieves state-of-the-art results. An important aspect of our method is that it can achieve good face recognition/verification performance by using raw 3D scans without any heavy preprocessing (i.e., model fitting, surface smoothing etc.).

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Section: Frgc V2mentioning

confidence: 96%

Section: Frgc V2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Learning from Normals for 3D Face Recognition

Marras

Zafeiriou

Tzimiropoulos

2012

Computer Vision – ECCV 2012. Workshops and Demonstrations

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show abstract

“…Romdhani and Vetter [17] extended this fitting algorithm, exploiting various image features such as edges or specular highlights. An analysis-by-synthesis approach was also recently used by Blanz et al [8] for fitting the model directly to a textured 3D scan, paying special attention to compensating the effects of unfavorable lighting.…”

Section: Related Workmentioning

confidence: 99%

Fast nonrigid mesh registration with a data-driven deformation prior

Schneider

Eisert

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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We propose an algorithm for non-rigidly registering a 3D template mesh with a dense point cloud, using a morphable shape model to control the deformation of the template mesh. A cost function involving nonrigid shape as well as rigid pose is proposed. Registration is performed by minimizing a first-order approximation of the cost function in the Iterative Closest Points framework. We show how a complex shape model, consisting of multiple PCA models for individual regions of the template, can be seamlessly integrated in the parameter estimation scheme. An appropriate Tikhonov regularization is introduced to guarantee the smoothness of the full mesh despite the splitting into local models. The proposed algorithm is compared to a recent generic nonrigid registration scheme. We show that the data-driven approach is faster, as the linear systems to be solved in the iterations are significantly smaller when a model is available. Also, we show that simultaneous optimization of pose and shape yields better registration results than shape alone.

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“…That is, we identify the parts (patches) of each 3D meshes that are relevant using an error distance and the direction of the normal vectors at each point of the face. Our approach allows finding specificities of persons that are not found with a conventional method of fitting [16]. We use this method both on the texture and on the depth data.…”

Section: Introductionmentioning

confidence: 99%

Patches Detection and Fusion for 3D Face Cloning

Manceau¹,

Soladié²,

Séguier³

2015

AIVP

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ABSTRACT3D face clones are used in many fields such as video games and Human-Computer Interaction. However, high-resolution sensors generating high quality clones are expensive and not accessible to all. In this paper, we propose to make a fully automated and accurate 3D reconstruction of a face with a low cost RGB-D camera. For each subject, we capture the depth and RGB data of their face in different positions while performing a rotational movement of the head. We fit a 3D Morphable Face Model on each frame to eliminate noise, increase resolution and provide a structured mesh. This type of mesh is a mesh which the semantic and topological structure is known. We propose to only keep the suitable parts of each mesh called Patch. This selection is performed using an error distance and the direction of the normal vectors. To create the 3D face clone, we merge the different patches of each mesh. These patches contain relevant information on the specificity of individuals and lead to the construction of a more accurate clone. We perform quantitative tests by comparing our clone to ground truth and qualitative tests by comparing visual features. These results show that our method outperforms the FaceWarehouse process of Cao et al [2]. This 3D face clone on a structured mesh can be used as pretreatment in applications such as emotion analysis [13] or facial animation.

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Fitting a Morphable Model to 3D Scans of Faces

Abstract: Abstract

Cited by 72 publications

References 28 publications

Robust Learning from Normals for 3D Face Recognition

Robust Learning from Normals for 3D Face Recognition

Fast nonrigid mesh registration with a data-driven deformation prior

Patches Detection and Fusion for 3D Face Cloning

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