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

27 Apr 2019Soshi ShimadaVladislav GolyanikChristian TheobaltDidier Stricker

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... (read more)

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