Tech Lead
Foundational GenAI Models
Adobe Firefly
I am the tech lead for core foundational models at Adobe Firefly, responsible for developing the next generation of Generative AI models. Previously, I was a Research Engineer at Adobe Research where I started the engineering efforts of Adobe Firefly and trained the first model that was released. Since then, I have led the development and training of the latest foundational imaging and video models powering Adobe Firefly's applications across various products and am working with teams across Adobe on improving these models to enable great user experiences and ship innovative applications.
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.
Adobe Firefly Video Model October 2024 Oversaw technical development of the video model including training of the models and data preparation. | Adobe Firefly Video GenExtend October 2024 Consulted on training and development approaches. | ||
Adobe Firefly Image Model v2 October 2023 Developed and trained parts of the model; oversaw overall technical development of the core diffusion model. | Adobe Firefly Image Model April 2023 Started the effort that would ultimately result in the foundation of Adobe Firefly; developed and trained the core diffusion model. |
For a full list, have a look at my Google Scholar page.
A novel and efficient approach for enabling personalized image generation with diffusion models.
C. Ham, M. Fisher, J. Hays, N. Kolkin, Y. Liu, R. Zhang, T. Hinz, Conference on Computer Vision and Pattern Recognition 2024.
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.
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.
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.
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.
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.
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.
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.