2 citations · 6 across the 6 of their papers we have counts for
6 papers
Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image
Shima Kamyab, Zohreh Azimifar
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 hu…
Survey of Deep Learning Methods for Inverse Problems
Shima Kamyab, Zohreh Azimifar, Rasool Sabzi +1
In this paper we investigate a variety of deep learning strategies for solving inverse problems. We classify existing deep learning solutions for inverse problems into three catego…
A New Approach for Optimizing Highly Nonlinear Problems Based on the Observer Effect Concept
Mojtaba Moattari, Emad Roshandel, Shima Kamyab +1
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 eq…
Deep Generative Models: Deterministic Prediction with an Application in Inverse Rendering
Shima Kamyab, Rasool Sabzi, Zohreh Azimifar
Deep generative models are stochastic neural networks capable of learning the distribution of data so as to generate new samples. Conditional Variational Autoencoder (CVAE) is a po…
End-to-end 3D shape inverse rendering of different classes of objects from a single input image
Shima Kamyab, S. Zohreh Azimifar
In this paper a semi-supervised deep framework is proposed for the problem of 3D shape inverse rendering from a single 2D input image. The main structure of proposed framework cons…
Deep Structure for end-to-end inverse rendering
Shima Kamyab, Ali Ghodsi, S. Zohreh Azimifar
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…