Title:In Between 3D Active Appearance Models and 3D Morphable Models
Authors: Jingu Heo, Marios Savvides
Abstract:
In this paper we propose a novel method of generating
3D Morphable Models (3DMMs) from 2D images. We
develop algorithms of 3D face reconstruction from a
sparse set of points acquired from 2D images. In order
to establish correspondence between images precisely, we
Combined Active Shape Models (ASMs) and Active Appearance
Models (AAMs)(CASAAMs) in an intelligent way,
showing improved performance on pixel-level accuracy
and generalization to unseen faces. The CASAAMs are
applied to the images of different views of the same person
to extract facial shapes across pose. These 2D shapes are
combined for reconstructing a sparse 3D model. The point
density of the model is increased by the Loop subdivision
method, which generates new vertices by a weighted sum
of the existing vertices. Then, the depth of the dense 3D
model is modified with an average 3D depth-map in order
to preserve facial structure more realistically. Finally, all
249 3D models with expression changes are combined
to generate a 3DMM for a compact representation. The
first session of the Multi-PIE database, consisting of 249
persons with expression and illumination changes, is used
for the modeling. Unlike typical 3DMMs, our model can
generate 3D human faces more realistically and efficiently
(2-3 seconds on P4 machine) under diverse illumination
conditions.
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