Hi! I’m Roman Bachmann, an EPFL PhD student at VILAB, where I’m advised by Amir Zamir. At the moment, I'm a Computer Vision & Machine Learning Intern at Apple. My current research is focused on building scalable multi-modal and multi-task models. I am also interested in generative modelling, world models, spatial memory, as well as novel view synthesis. My goal is to build adaptable world priors that enable quick understanding of the environment and allow for global, out-of-sight reasoning.

I received my M.Sc. degree in Data Science at EPFL, where I also previously completed my B.Sc. in Computer Science.

Publications

2024

4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities

Roman Bachmann*, Oguzhan Fatih Kar*, David Mizrahi*, Ali Garjani, Mingfei Gao, David Griffiths, Jiaming Hu, Afshin Dehghan, Amir Zamir

NeurIPS 2024

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ViPer: Visual Personalization of Generative Models via Individual Preference Learning

Sogand Salehi, Mahdi Shafiei, Teresa Yeo, Roman Bachmann, Amir Zamir

ECCV 2024

PDF   |   Website   |   Code

2023

4M: Massively Multimodal Masked Modeling

David Mizrahi*, Roman Bachmann*, Oguzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir

NeurIPS 2023

Spotlight

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Modality-invariant Visual Odometry for Embodied Vision

Marius Memmel, Roman Bachmann, Amir Zamir

2023 Conference on Computer Vision and Pattern Recognition

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2022

MultiMAE: Multi-modal Multi-task Masked Autoencoders

Roman Bachmann*, David Mizrahi*, Andrei Atanov, Amir Zamir

2022 European Conference on Computer Vision

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CLIPasso: Semantically-Aware Object Sketching

Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, Ariel Shamir

SIGGRAPH 2022

Selected as one of the five Best Paper Award winners

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2021

Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans

Ainaz Eftekhar*, Alexander Sax*, Roman Bachmann, Jitendra Malik, Amir Zamir

2021 International Conference on Computer Vision

PDF   |   Website

2020

Training Binary Neural Networks using the Bayesian Learning Rule

Xiangming Meng, Roman Bachmann, and Mohammad Emtiyaz Khan

2020 International Conference on Machine Learning

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2019

Motion Capture from Pan-Tilt Cameras with Unknown Orientation

Roman Bachmann, Jörg Spörri, Pascal Fua, and Helge Rhodin

2019 International Conference on 3D Vision

Selected for oral presentation

PDF   |   Dataset
Global Motion Estimation from Pan-Tilt Cameras

Roman Bachmann, Helge Rhodin, and Pascal Fua

2019 Central European Seminar on Computer Graphics (non-peer-reviewed)

Voted best presentation and third best paper

PDF   |   Dataset
Automatic 3D motion capture in alpine skiing using deep learning and computer vision

Roman Bachmann, Helge Rhodin, Jörg Spörri, and Pascal Fua

8th Int. Congress on Science and Skiing (non-peer-reviewed)

Won first place in the Young Investigator Award competition

PDF   |   Dataset