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Learning to Fit Morphable Models

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13666))

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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 three problems, 3D body estimation from a head-mounted device, 3D body estimation from sparse 2D keypoints and face surface estimation from dense 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.

V. Choutas—Work performed at Microsoft.

F. Bogo—Now at Meta Reality Labs Research.

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Acknowledgement

We thank Pashmina Cameron, Sadegh Aliakbarian, Tom Cashman, Darren Cosker and Andrew Fitzgibbon for valuable discussions and proof reading.

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Authors and Affiliations

  1. Max Planck Institute for Intelligent Systems, Tübingen, Germany

    Vasileios Choutas

  2. Microsoft, Redmond, USA

    Vasileios Choutas, Federica Bogo, Jingjing Shen & Julien Valentin

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  1. Vasileios Choutas
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  2. Federica Bogo
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  3. Jingjing Shen
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  4. Julien Valentin
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Correspondence to Vasileios Choutas .

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  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Choutas, V., Bogo, F., Shen, J., Valentin, J. (2022). Learning to Fit Morphable Models. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_10

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