Search Results for author: Mehdi S. M. Sajjadi

Found 13 papers, 4 papers with code

RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs

no code implementations1 Dec 2021 Michael Niemeyer, Jonathan T. Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, Noha Radwan

We observe that the majority of artifacts in sparse input scenarios are caused by errors in the estimated scene geometry, and by divergent behavior at the start of training.

Novel View Synthesis

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations

no code implementations25 Nov 2021 Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, Jakob Uszkoreit, Thomas Funkhouser, Andrea Tagliasacchi

In this work, we propose the Scene Representation Transformer (SRT), a method which processes posed or unposed RGB images of a new area, infers a "set-latent scene representation", and synthesises novel views, all in a single feed-forward pass.

Novel View Synthesis Semantic Segmentation

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

1 code implementation CVPR 2021 Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs.

From Variational to Deterministic Autoencoders

3 code implementations ICLR 2020 Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, Bernhard Schölkopf

Variational Autoencoders (VAEs) provide a theoretically-backed and popular framework for deep generative models.

Density Estimation

Perceptual Video Super Resolution with Enhanced Temporal Consistency

no code implementations20 Jul 2018 Eduardo Pérez-Pellitero, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Schölkopf

Together with a video discriminator, we also propose additional loss functions to further reinforce temporal consistency in the generated sequences.

Image Super-Resolution Video Super-Resolution

Assessing Generative Models via Precision and Recall

4 code implementations NeurIPS 2018 Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

Recent advances in generative modeling have led to an increased interest in the study of statistical divergences as means of model comparison.

Tempered Adversarial Networks

no code implementations ICML 2018 Mehdi S. M. Sajjadi, Giambattista Parascandolo, Arash Mehrjou, Bernhard Schölkopf

A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset.

Frame-Recurrent Video Super-Resolution

no code implementations CVPR 2018 Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown

Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images.

Motion Compensation Multi-Frame Super-Resolution +1

Depth Estimation Through a Generative Model of Light Field Synthesis

no code implementations6 Sep 2016 Mehdi S. M. Sajjadi, Rolf Köhler, Bernhard Schölkopf, Michael Hirsch

Light field photography captures rich structural information that may facilitate a number of traditional image processing and computer vision tasks.

Depth Estimation

Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines

no code implementations2 Jun 2015 Mehdi S. M. Sajjadi, Morteza Alamgir, Ulrike Von Luxburg

Peer grading is the process of students reviewing each others' work, such as homework submissions, and has lately become a popular mechanism used in massive open online courses (MOOCs).

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