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Found 6 papers, 1 papers with code
Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image
In this paper, a low parameter deep learning framework utilizing the Non-metric Multi-Dimensional scaling (NMDS) method, is proposed to recover the 3D shape of 2D landmarks on a human face, in a single input image.
Survey of Deep Learning Methods for Inverse Problems
In this paper we investigate a variety of deep learning strategies for solving inverse problems.
A New Approach for Optimizing Highly Nonlinear Problems Based on the Observer Effect Concept
A lot of real-world engineering problems represent dynamicity with nests of nonlinearities due to highly complex network of exponential functions or large number of differential equations interacting together.
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering
Deep generative models are stochastic neural networks capable of learning the distribution of data so as to generate new samples.
End-to-end 3D shape inverse rendering of different classes of objects from a single input image
The main structure of proposed framework consists of unsupervised pre-trained components which significantly reduce the need to labeled data for training the whole framework.
Deep Structure for end-to-end inverse rendering
Inverse rendering in a 3D format denoted to recovering the 3D properties of a scene given 2D input image(s) and is typically done using 3D Morphable Model (3DMM) based methods from single view images.
