English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

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

MPS-Authors
/persons/resource/persons239654

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

/persons/resource/persons45610

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

Locator
There are no locators available
Fulltext (public)

arXiv:1904.12144.pdf
(Preprint), 6MB

Supplementary Material (public)
There is no public supplementary material available
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: http://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.