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Paper

IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction

MPS-Authors
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Golyanik,  Vladislav
Computer Graphics, MPI for Informatics, Max Planck Society;

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Theobalt,  Christian       
Computer Graphics, MPI for Informatics, Max Planck Society;

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arXiv:1904.12144.pdf
(Preprint), 6MB

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Citation

Shimada, S., Golyanik, V., Theobalt, C., & Stricker, D. (2019). IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction. Retrieved from http://arxiv.org/abs/1904.12144.


Cite as: https://hdl.handle.net/21.11116/0000-0003-FE04-B
Abstract
The majority of the existing methods for non-rigid 3D surface regression from
monocular 2D images require an object template or point tracks over multiple
frames as an input, and are still far from real-time processing rates. In this
work, we present the Isometry-Aware Monocular Generative Adversarial Network
(IsMo-GAN) - an approach for direct 3D reconstruction from a single image,
trained for the deformation model in an adversarial manner on a light-weight
synthetic dataset. IsMo-GAN reconstructs surfaces from real images under
varying illumination, camera poses, textures and shading at over 250 Hz. In
multiple experiments, it consistently outperforms several approaches in the
reconstruction accuracy, runtime, generalisation to unknown surfaces and
robustness to occlusions. In comparison to the state-of-the-art, we reduce the
reconstruction error by 10-30% including the textureless case and our surfaces
evince fewer artefacts qualitatively.