Photo-realistic 3D Head Modeling Using Multi-view Images

Lee, Tong‐Yee; Lin, Ping-Hsien; Yang, Tz-Hsien

doi:10.1007/978-3-540-24709-8_75

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…Their method takes advantage of a rough knowledge of the head's shape (from a generic face model), but it is computationally expensive because it requires dense stereo matching. In a similar approach, Lee et al [20] constructed a 3D head model using two-pass bundle adjustments. In the first pass, the method computes several feature points of a target 3D head and then uses these features to obtain a roughly matched head model by modifying a generic head.…”

Section: Preliminary Studymentioning

confidence: 99%

3D Face Modeling Using the Multi-Deformable Method

Hwang

Kim

et al. 2012

Sensors

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In this paper, we focus on the problem of the accuracy performance of 3D face modeling techniques using corresponding features in multiple views, which is quite sensitive to feature extraction errors. To solve the problem, we adopt a statistical model-based 3D face modeling approach in a mirror system consisting of two mirrors and a camera. The overall procedure of our 3D facial modeling method has two primary steps: 3D facial shape estimation using a multiple 3D face deformable model and texture mapping using seamless cloning that is a type of gradient-domain blending. To evaluate our method's performance, we generate 3D faces of 30 individuals and then carry out two tests: accuracy test and robustness test. Our method shows not only highly accurate 3D face shape results when compared with the ground truth, but also robustness to feature extraction errors. Moreover, 3D face rendering results intuitively show that our method is more robust to feature extraction errors than other 3D face modeling methods. An additional contribution of our method is that a wide range of face textures can be acquired by the mirror system. By using this texture map, we generate realistic 3D face for individuals at the end of the paper.

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Section: Preliminary Studymentioning

confidence: 99%

3D Face Modeling Using the Multi-Deformable Method

Hwang

Kim

et al. 2012

Sensors

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“…Regarding 3D head reconstruction from 2D data, some methods [LMT98, LLY04] obtain low resolution complete and textured 3D head models. Also in these cases, geometry is not accurate enough for our aim.…”

Section: Related Workmentioning

confidence: 99%

Reconstructing head models from photographs for individualized 3D‐audio processing

Dellepiane

Pietroni

Tsingos

et al. 2008

Computer Graphics Forum

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Visual fidelity and interactivity are the main goals in Computer Graphics research, but recently also audio is assuming an important role. Binaural rendering can provide extremely pleasing and realistic three-dimensional sound, but to achieve best results it's necessary either to measure or to estimate individual Head Related Transfer Function (HRTF). This function is strictly related to the peculiar features of ears and face of the listener. Recent sound scattering simulation techniques can calculate HRTF starting from an accurate 3D model of a human head. Hence, the use of binaural rendering on large scale (i.e. video games, entertainment) could depend on the possibility to produce a sufficiently accurate 3D model of a human head, starting from the smallest possible input. In this paper we present a completely automatic system, which produces a 3D model of a head starting from simple input data (five photos and some key-points indicated by user). The geometry is generated by extracting information from images and accordingly deforming a 3D dummy to reproduce user head features. The system proves to be fast, automatic, robust and reliable: geometric validation and preliminary assessments show that it can be accurate enough for HRTF calculation.

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“…This will save a lot of resources and efforts. Or, cinematographers can usually create 3D computational scenes and environments, perform complex explosions and special tricks on computers and then merge scenes together as well as transform some actors/ actresses into other characters or even make certain transformations on the actors/ actresses themselves (Ansari and Abdel-Mottaleb, 2003;Hwang et al, 2011;Lee et al, 1995;Zhang et al, 2002;Fua, 1997;Blanz and Vetter, 1999;Akimoto et al, 1993;Ip and Yin, 1996;Lee et al, 2004;Lin et al, 2002). Moreover, there are still many professional applications relating to manipulating the object deformation and rectification (Fan et al, 2017;Rezende et al, 2016;Rock et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

An innovative approach to automatically identify control point set for model deformation rectification

al.¹

2019

Int. j. adv. appl. sci.

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Rectifying 3D model deformation based on control point set is one of the most important problems frequently applied in virtual reality fields, in which control point set is the key for implementing deformed manipulation. This paper presents a technique to automatically identify control point set by analyzing the changes of each point in a 3D model through its variations, then gathering clusters and selecting important points to become a control point set. Our proposed approach was tested and proved to be effective in our practical experiments with a deforming technique based on the interpolation of the radial basis function.

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Photo-realistic 3D Head Modeling Using Multi-view Images

Cited by 8 publications

References 5 publications

3D Face Modeling Using the Multi-Deformable Method

3D Face Modeling Using the Multi-Deformable Method

Reconstructing head models from photographs for individualized 3D‐audio processing

An innovative approach to automatically identify control point set for model deformation rectification

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