Learning to Fit Morphable Models
- URL: http://arxiv.org/abs/2111.14824v1
- Date: Mon, 29 Nov 2021 18:59:53 GMT
- Title: Learning to Fit Morphable Models
- Authors: Vasileios Choutas, Federica Bogo, Jingjing Shen, Julien Valentin
- Abstract summary: We build upon recent advances in learned optimization and propose an update rule inspired by the classic Levenberg-Marquardt algorithm.
We show the effectiveness of the proposed neural on the problems of 3D body surface estimation from a head-mounted device and face fitting from 2D landmarks.
- Score: 12.469605679847085
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fitting parametric models of human bodies, hands or faces to sparse input
signals in an accurate, robust, and fast manner has the promise of
significantly improving immersion in AR and VR scenarios. A common first step
in systems that tackle these problems is to regress the parameters of the
parametric model directly from the input data. This approach is fast, robust,
and is a good starting point for an iterative minimization algorithm. The
latter searches for the minimum of an energy function, typically composed of a
data term and priors that encode our knowledge about the problem's structure.
While this is undoubtedly a very successful recipe, priors are often hand
defined heuristics and finding the right balance between the different terms to
achieve high quality results is a non-trivial task. Furthermore, converting and
optimizing these systems to run in a performant way requires custom
implementations that demand significant time investments from both engineers
and domain experts. In this work, we build upon recent advances in learned
optimization and propose an update rule inspired by the classic
Levenberg-Marquardt algorithm. We show the effectiveness of the proposed neural
optimizer on the problems of 3D body surface estimation from a head-mounted
device and face fitting from 2D landmarks. Our method can easily be applied to
new model fitting problems and offers a competitive alternative to well tuned
'traditional' model fitting pipelines, both in terms of accuracy and speed.
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