Oswald Aldrian

The University of York, York, England, United Kingdom

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Publications (7)5.78 Total impact

  • Oswald Aldrian · William A.P. Smith
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    ABSTRACT: In this paper, we present a complete framework to inverse render faces with a 3D Morphable Model (3DMM). By decomposing the image formation process into geometric and photometric parts, we are able to state the problem as a multilinear system which can be solved accurately and efficiently. As we treat each contribution as independent, the objective function is convex in the parameters and a global solution is guaranteed. We start by recovering 3D shape using a novel algorithm which incorporates generalization error of the model obtained from empirical measurements. We then describe two methods to recover facial texture, diffuse lighting, specular reflectance, and camera properties from a single image. The methods make increasingly weak assumptions and can be solved in a linear fashion. We evaluate our findings on a publicly available database, where we are able to outperform an existing state-of-the-art algorithm. We demonstrate the usability of the recovered parameters in a recognition experiment conducted on the CMU-PIE database.
    No preview · Article · May 2013 · IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Oswald Aldrian · William A. P. Smith
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    ABSTRACT: In this paper we consider the problem of inverse rendering faces under unknown environment illumination using a morphable model. In contrast to previous approaches, we account for global illumination effects by incorporating statistical models for ambient occlusion and bent normals into our image formation model. We show that solving for ambient occlusion and bent normal parameters as part of the fitting process improves the accuracy of the estimated texture map and illumination environment. We present results on challenging data, rendered under complex natural illumination with both specular reflectance and occlusion of the illumination environment.
    No preview · Conference Paper · Oct 2012
  • Oswald Aldrian · William A. P. Smith
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    ABSTRACT: We present a novel framework to inverse render faces in arbitrary complex illumination with a 3D morphable model. Compared to previously introduced methods, we specifically take self-occlusion into account and demonstrate that this improves the fitting accuracy by about 10%. Motivated by this observation, we design a generative statistical model of ambient occlusion. We examine generalisation error of the model and propose two ways how ambient occlusion can be inferred from shape. The proposed methods are incorporated into an existing framework to inverse render faces. We show qualitative and quantitative results for the proposed extensions and compare it with a reference method.
    No preview · Conference Paper · Jun 2012
  • Oswald Aldrian · William A. P. Smith
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    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.
    No preview · Conference Paper · Nov 2011
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    Oswald Aldrian · William A P Smith
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    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.
    Preview · Article · Jan 2011
  • Oswald Aldrian · William A. P. Smith
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    ABSTRACT: In this paper, we present a new method to statistically recover the full 3D shape of a face from a set of sparse feature points. We attribute noise in the feature point positions to generalisation error of the model. We learn the variance of these feature points empirically using out-of-sample data. This allows the shape reconstruction to probabilistically model the way in which feature points deviate from their true position. We are able to reduce the reconstruction error by as much as 12%.
    No preview · Conference Paper · Oct 2010
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    Oswald Aldrian · William A. P. Smith

    Full-text · Conference Paper · Jan 2010

Publication Stats

32 Citations
5.78 Total Impact Points


  • 2010-2013
    • The University of York
      • Department of Computer Science
      York, England, United Kingdom
    • CUNY Graduate Center
      New York City, New York, United States