Estimating coloured 3D face models from single images: An example based approach

Vetter, Thomas; Blanz, Volker

doi:10.1007/bfb0054761

Cited by 68 publications

(47 citation statements)

References 16 publications

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“…To compare our TPS method with the original ICP algorithm [7] and the optical flow method [9], the MPI source 3D face is aligned to the target 3D face using these three methods, respectively. To compute the point correspondence by the optical flow method, the source and target 3D faces are spread into texture and height mapping images (shown in Figure 4) by cylinder coordinate transformation.…”

Section: Experimental Results Of 3d Face Morphing and Animationmentioning

confidence: 99%

“…Blanz and Vetter made dense correspondence between 3D facial scans [1,9], taking advantage of the fact that the radial coordinate from Cyberware scans can be expressed as a height map image with the intensity representing the radius in cylinder coordinate system. They used optical flow technique to establish correspondence between texture images and height maps images, and the correspondence was refined by a bootstrapping method if large amount of the prototypic scans obtained.…”

Section: Related Workmentioning

confidence: 99%

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A Dense Point‐to‐Point Alignment Method for Realistic 3D Face Morphing and Animation

Zhou

2009

International Journal of Computer Games Technology

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We present a new point matching method to overcome the dense point-to-point alignment of scanned 3D faces. Instead of using the rigid spatial transformation in the traditional iterative closest point (ICP) algorithm, we adopt the thin plate spline (TPS) transformation to model the deformation of different 3D faces. Because TPS is a non-rigid transformation with good smooth property, it is suitable for formulating the complex variety of human facial morphology. A closest point searching algorithm is proposed to keep one-to-one mapping, and to get good efficiency the point matching method is accelerated by a KD-tree method. Having constructed the dense point-to-point correspondence of 3D faces, we create 3D face morphing and animation by key-frames interpolation and obtain realistic results. Comparing with ICP algorithm and the optical flow method, the presented point matching method can achieve good matching accuracy and stability. The experiment results have shown that our method is efficient for dense point objects registration.

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Section: Experimental Results Of 3d Face Morphing and Animationmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Dense Point‐to‐Point Alignment Method for Realistic 3D Face Morphing and Animation

Zhou

2009

International Journal of Computer Games Technology

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show abstract

“…Since the topology of the generic model remains constant, the FAT need not be changed for different people. In principle, this table also allows to use different parameter sets and more efficient orthogonal facial expression representations like those proposed in [31] and [16].…”

Section: A Facial Expression Synthesis and Animationmentioning

confidence: 99%

MPEG‐4 facial animation in video analysis and synthesis

Eisert

2003

Int J Imaging Syst Tech

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Abstract-MPEG-4 supports the definition, encoding, transmission, and animation of 3-D head and body models. These features can be used for a variety of different applications ranging from low bit-rate video coding to character and avatar animation. In this paper, an entire system for the analysis of facial expressions from image sequences and their synthesis is presented. New methods for the estimation of MPEG-4 facial animation parameters as well as scene illumination are proposed. Experiments for different applications demonstrate the potential of using facial animation techniques in video analysis and synthesis. A model-based codec is presented that is able to encode head-and-shoulder video sequences at bit-rates of about 1 kbit/s. Besides the low bit-rate, many enhancements and scene modifications can be easily applied, like scene lighting changes or cloning of expressions for character animation. But also for the encoding of arbitrary sequences, 3-D knowledge can help to increase the coding efficiency. With our model-aided codec, bit-rate reductions of up to 45 % at the same quality can be achieved in comparison to standard hybrid video codecs.

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“…Though those methods can directly obtain the 3-D information, it only performs well under limited circumstances. Another approach is to use a linear class of objects; Vetter and Blanz [17] used a linear class of both images and 3-D geometrics for image matching and face modeling. It can eliminate most of the nonnatural faces and significantly reduces the search space.…”

Section: Introductionmentioning

confidence: 99%

Learning and synthesizing MPEG-4 compatible 3-D face animation from video sequence

Gao

Chen²,

Wang

et al. 2003

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-In this paper, we present a new system that applies an example-based learning method to learn facial motion patterns from a video sequence of individual facial behavior such as lip motion and facial expressions, and using that to create vivid threedimensional (3-D) face animation according to the definition of MPEG-4 face animation parameters. The system consists of three key modules, face tracking, pattern learning, and face animation. In face tracking, to reduce the complexity of the tracking process, a novel coarse-to-fine strategy combined with a Kalman filter is proposed for localizing key facial landmarks in each image of the video. The landmarks' sequence is normalized into a visual feature matrix and then fed to the next step of process. In pattern learning, in the pretraining stage, the parameters of the camera that took the video are requested with the training video data so the system can estimate the basic mapping from a normalized two-dimensional (2-D) visual feature matrix to the representation in 3-D MPEG-4 face animation parameter space, in assistance with the computer vision method. In the practice stage, considering that in most cases camera parameters are not provided with video data, the system uses machine learning technology to complement the incomplete 3-D information for the mapping that information is needed in face orientation presentation. The example-based learning in this system integrates several methods including clustering, HMM, and ANN to make a better conversion from a 2-D to 3-D model and better estimation of incomplete 3-D information for good mapping; this will be used to drive face animation thereafter. In face animation, the system can synthesize face animation following any type of face motion in video. Experiments show that our system produces more vivid face motion animation, compared to other early systems.

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Estimating coloured 3D face models from single images: An example based approach

Cited by 68 publications

References 16 publications

A Dense Point‐to‐Point Alignment Method for Realistic 3D Face Morphing and Animation

A Dense Point‐to‐Point Alignment Method for Realistic 3D Face Morphing and Animation

MPEG‐4 facial animation in video analysis and synthesis

Learning and synthesizing MPEG-4 compatible 3-D face animation from video sequence

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