TOBIAS HINZ
Machine Learning
Research Engineer
Adobe Research
I am a machine learning research engineer at Adobe Research in California, working on generative models for image generation and editing. I am interested in developing models that allow generation and editing of images on general domains. Currently, I am working on large-scale generative diffusion models for Adobe Firefly for which I am the tech lead on the modeling side and one of the founding members.
I obtained my PhD at the Knowledge Technology group at the University of Hamburg (Germany). Before that, I completed a research oriented Master's degree in Intelligent Adaptive Systems at the University of Hamburg. I received a Bachelor's degree in Business Informatics at the University of Mannheim, during which I also studied at the National University of Singapore for one semester.
News
- 03/2023: Our Project Firefly was announced today.
- 02/2023: Our paper on improved inpaiting with diffusion models was accepted as a highlight to CVPR 2023.
- 05/2022: Our paper on transformers for image editing has been accepted to SIGGRAPH 2022.
- 05/2022: I have moved to San Jose (California) to join the Adobe Headquarters office.
- 01/2022: Our paper won the Best Application Paper award at WACV 2022.
- 09/2021: Our review paper on adversarial text-to-image synthesis has been published by the Neural Networks Journal.
- 08/2021: Check out this concert which combines music with generated videos. Our ConSinGAN approach was used in the video for "Robbie's Hobbies.
- 04/2021: I successfully defended my PhD thesis.
- 02/2021: Have a look at our paper on Few-Shot Character Animation (code available).
- 01/2021: Check out our latest review paper on Adversarial Text-to-Image Synthesis.
- 01/2021: I started a new position as machine learning research engineer with Adobe Research.
Selected Publications
For a full list, have a look at my Google Scholar page.
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model
A diffusion model for shape-guided inpainting with better shape control and background preservation within the inpainted region.
S. Xie, Z. Zhang, Z. Lin, T. Hinz, K. Zhang, Conference on Computer Vision and Pattern Recognition 2023.
ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
A neural architecture for automatically modifying an input high-resolution image according to a user's edits on its semantic segmentation map.
D. Liu, S. Shetty, T. Hinz, M. Fisher, R. Zhang, T. Park, E. Kalogerakis, ACM Transactions on Graphics (SIGGRAPH 2022) 2022.
Speeding Up the Hyperparameter Optimization Of Deep Convolutional Neural Networks
How to use lower dimensional data representations to speed up the hyperparameter optimization for CNNs processing images..
T. Hinz, N. Navarro-Guerrero, S. Magg, S. Wermter, International Journal of Computational Intelligence and Applications 2018.
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My Remote Internship with Adobe Research (Summer 2020)
Posted on 18 Sep 2020 | Reading time: 3 minutes
I had the chance to do an internship with Adobe Research in San-Francisco. Due to COVID all Adobe staff are working from home and so I did my internship remotely from Germany.
Improved Techniques for Training Single-Image GANs
Posted on 24 Mar 2020 | Reading time: 7 minutes
Overview of our paper about training GANs on a single image for tasks such as image generation, image harmonization, and image animation.
Semantic Object Accuracy for Generative Text-to-Image Synthesis
Posted on 30 Oct 2019 | Reading time: 8 minutes
Overview of our paper about our new model and evaluation metric for generative text-to-image synthesis models.
My Time At The Deep Learning And Reinforcement Learning Summer School (DLRLSS) in Edmonton (Canada)
Posted on 06 Aug 2019 | Reading time: 17 minutes
After attending the EEMLSS I was lucky enough to also attend the Deep Learning And Reinforcement Learning Summer School (DLRLSS) in Edmonton (Canada) 2019.
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