Fitting 3D morphable models using implicit representations

We consider the problem of approximating the 3D scan of a real object through an affine combination of examples. Common approaches depend either on the explicit estimation of point-to-point correspondences or on 2-dimensional projections of the target mesh; both present drawbacks. We follow an approach similar to [IF03] by representing the target via an implicit function, whose values at the vertices of the approximation are used to define a robust cost function. The problem is approached in two steps, by approximating first a coarse implicit representation of the whole target, and then finer, local ones; the local approximations are then merged together with a Poisson-based method. We report the results of applying our method on a subset of 3D scans from the Face Recognition Grand Challenge v.1.0.

Determinants of Advertising Effectiveness: The Development of an International Advertising Elasticity Database and a Meta-Analysis

Increasing demand for marketing accountability requires an efficient allocation of marketing expenditures. Managers who know the elasticity of their marketing instruments can allocate their budgets optimally. Meta-analyses offer a basis for deriving benchmark elasticities for advertising. Although they provide a variety of valuable insights, a major shortcoming of prior meta-analyses is that they report only generalized results as the disaggregated raw data are not made available. This problem is highly relevant because coding of empirical studies, at least to a certain extent, involves subjective judgment. For this reason, meta-studies would be more valuable if researchers and practitioners had access to disaggregated data allowing them to conduct further analyses of individual, e.g., product-level-specific, interests. We are the first to address this gap by providing (1) an advertising elasticity database (AED) and (2) empirical generalizations about advertising elasticities and their determinants. Our findings indicate that the average current-period advertising elasticity is 0.09, which is substantially smaller than the value 0f 0.12 that was recently reported by Sethuraman, Tellis, and Briesch (2011). Furthermore, our meta-analysis reveals a wide range of significant determinants of advertising elasticity. For example, we find that advertising elasticities are higher (i) for hedonic and experience goods than for other goods; (ii) for new than for established goods; (iii) when advertising is measured in gross rating points (GRP) instead of absolute terms; and (iv) when the lagged dependent or lagged advertising variable is omitted.