Mehdi S. M. Sajjadi
Mehdi S. M. Sajjadi is a team lead in Google DeepMind (Berlin, Germany) working on 3D-aware neural scene representations and deep generative models. Previously, he has been a PhD student at the Max Planck Institute for Intelligent Systems with Prof. Dr. Bernhard Schölkopf with an associated fellowship at the ETH Center for Learning Systems, after studying computer science & math at the University of Hamburg, receiving a MSc with distinction. His research on machine learning and computer vision has been published at several renowned conferences including NeurIPS, ICML, ICLR, CVPR, ICCV, and ECCV.
For more information and an up-to-date list of publications, please visit msajjadi.com.
For more information and an up-to-date list of publications, please visit msajjadi.com.
Research Areas
Authored Publications
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Google
DORSal: Diffusion for Object-centric Representations of Scenes et al.
Allan Jabri
Emiel Hoogeboom
Thomas Kipf
International Conference on Learning Representations (2024)
Test-time Adaptation with Slot-centric Models
Mihir Prabhudesai
Anirudh Goyal
Gaurav Aggarwal
Thomas Kipf
Deepak Pathak
Katerina Fragkiadaki
International Conference on Machine Learning (2023), pp. 28151-28166
RUST: Latent Neural Scene Representations from Unposed Imagery
Thomas Kipf
Klaus Greff
Conference on Computer Vision and Pattern Recognition (CVPR) (2023) (to appear)
Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames
Ondrej Biza
Gamaleldin Elsayed
Thomas Kipf
International Conference on Machine Learning (2023), pp. 2507-2527
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
Henning Meyer
Urs Bergmann
Klaus Greff
Noha Radwan
Alexey Dosovitskiy
Jakob Uszkoreit
Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
Michael Niemeyer
Ben Mildenhall
Andreas Geiger
Noha Radwan
Computer Vision and Pattern Recognition (CVPR) (2022)
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes
Noha Radwan*
Klaus Greff
Henning Meyer
Kyle Genova
Transactions on Machine Learning Research (2022)
Kubric: A scalable dataset generator
Anissa Yuenming Mak
Austin Stone
Carl Doersch
Cengiz Oztireli
Charles Herrmann
Daniel Rebain
Derek Nowrouzezahrai
Dmitry Lagun
Fangcheng Zhong
Florian Golemo
Francois Belletti
Henning Meyer
Hsueh-Ti (Derek) Liu
Issam Laradji
Klaus Greff
Kwang Moo Yi
Lucas Beyer
Matan Sela
Noha Radwan
Thomas Kipf
Tianhao Wu
Vincent Sitzmann
Yilun Du
Yishu Miao
(2022)
Object Scene Representation Transformer
Filip Pavetić
Leonidas Guibas
Klaus Greff
Thomas Kipf
Advances in Neural Information Processing Systems (2022), pp. 9512-9524
