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In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.
In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.
In this paper, we demonstrate an alternative solution that is based on the idea of encoding images into a latent non-linear representation of meshes.
In this paper, we present a new perspective towards image-based shape generation.
We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN).
We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras.
In the first stage, we train a CNN to map each pixel to an embedding space where pixels from the same plane instance have similar embeddings.
This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space"