Conference Paper

A Linear Approach to Face Shape and Texture Recovery using a 3D Morphable Model.

DOI: 10.5244/C.24.75 Conference: British Machine Vision Conference, BMVC 2010, Aberystwyth, UK, August 31 - September 3, 2010. Proceedings
Source: DBLP
Download full-text


Available from: William A. P. Smith, Oct 17, 2014
9 Reads
  • Source
    • "The key advantage of this approach is that shape and texture estimation can both be posed as multilinear (and hence convex) fitting problems and solved independently. We begin by estimating 3D shape parameters and the camera matrix by fitting to sparse feature points using the algorithm proposed in [1]. This approach uses an empirical model of the generalisation capability of each feature point. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Well known results in inverse rendering show that recovery of unconstrained illumi-nation, texture and reflectance properties from a single image is ill-posed. On the other hand, in the domain of faces linear statistical models have been shown to efficiently characterise variations in face shape and texture. In this paper we show how the inverse rendering process can be constrained using a morphable model of face shape and tex-ture. Starting with a shape estimate recovered using the statistical shape model, we show that the image formation process leads to a system of equations which is multilinear in the unknowns. We are able to estimate diffuse texture, specular reflectance properties, the illumination environment and camera properties from a single image. Our approach uses relaxed assumptions and offers improved performance in comparison to the current state-of-the-art morphable model fitting algorithms.
  • [Show abstract] [Hide abstract]
    ABSTRACT: 3D face shape provides a pose and illumination invariant description of human faces. In this paper, we propose a novel component based method to recover the full 3D face shape from a set of sparse feature points. We use a local linear fitting (LLF) scheme so that reconstruction of each subregion depends on both its own vertices and adjacent subregions. This method results in a separate set of shape coefficients each emphasizing the quality of one subregion and improves the model expressiveness. Experiments show that the LLF strategy significantly reduces the model residual error, and thus reduces the sparse reconstruction error under pose variations. Moreover, the problem of estimating pose parameters is revisited, and we use a joint optimization method to improve the reconstruction quality under unknown pose. We evaluate the sensitivity of our method to the selection of feature points. Simulation results show that our method is more robust than prevailing methods.
    The Visual Computer 02/2014; 30(2). DOI:10.1007/s00371-013-0795-3 · 0.96 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, we consider the problem of inverse rendering in the case where surface texture can be approximated by a linear basis. Assuming a dichromatic reflectance model, we show that spherical harmonic illumination coefficients and texture parameters can be estimated in a specular invariant colour subspace by solving a system of bilinear equations. We focus on the case of faces, where both shape and texture can be efficiently described by a linear statistical model. In this context, we are able to fit a 3D morphable model to a single colour image, accounting for both non-Lambertian specular reflectance and complex illumination of the same light source colour. We are able to recover statistical texture model parameters with an accuracy comparable to more computationally expensive analysis-by-synthesis approaches. Moreover, our approach requires only the solution of convex optimisation problems.
    IEEE International Conference on Computer Vision Workshops, ICCV 2011 Workshops, Barcelona, Spain, November 6-13, 2011; 01/2011
Show more