Generative AI

Generative AI focuses on models that can synthesize, reconstruct, and reason about complex data by learning underlying structure and variability. In our research, we explore generative methods and large reconstruction models (LRMs) that operate across images and geometry. These models support tasks such as image and shape synthesis, reconstruction, and neural mesh generation.

In medical AI, generative models are applied to produce accurate and clinically meaningful reconstructions from medical data. This includes medical image super-resolution for modalities such as CT, where generative approaches enhance spatial detail, as well as neural mesh generation for reconstructing anatomical structures. These representations enable patient-specific modeling and implant research, supporting personalized analysis, design, and simulation.

This work is conducted as part of the Interdisciplinary Center for Scientific Computing and Imaging (IDIR) at Kiel University (link), fostering close collaboration between AI, medicine, and engineering.

Generative neural networks for medical super-resolution.

Selected Publications:

N. C. Koser, M. J. Finck, F. N. von Brackel, B. Ondruschka, S. Pirk, C.-C. Glüer, Real Super-Resolution
for Proximal Femur: Enhanced Computation of Structural Bone Metrics from Clinical CTs, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2025)
[Preprint], [Poster]

M. J. Finck, N. C. Koser, J. B. Hövener, C. C. Glüer, S. Pirk, FemoraLyze: A Modular Framework for Proximal Femur Analysis, Medical Imaging with Deep Learning-Short Papers, 2025
[Website], [Preprint], [Poster]

D. Xie, S. Bi, Z. Shu, K. Zhang, Z. Xu, Y. Zhou, S. Pirk, A. Kaufman, X. Sun, H. Tan, LRM-Zero: Training Large Reconstruction Models with Synthesized Data, Conference on Neural Information Processing Systems (NeurIPS), 2024
[Website], [ArXiv], [Preprint]

A. Maesumi, D. Hu, K. Saripalli, V. G. Kim, M. Fisher, S. Pirk, D. Ritchie, One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns, ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2024
[Website], [ArXiv], [Preprint], [Bibtex]

D. Xie, J. Li, H. Tan, X. Sun, Z. Shu, Y. Zhou, S. Bi, S. Pirk, A. E. Kaufman, Carve3D: Improving Multi-view Reconstruction Consistency for Diffusion Models with RL Finetuning, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024
[Website], [Preprint], [ArXiv], [Bibtex]

Z. Zhu, Y. Li, W. Lyu, K. Kumar Singh, Z. Shu, S. Pirk, D. Hoiem, Consistent Multimodal Generation via a Unified GAN Framework, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024
[Preprint], [ArXiv], [Bibtex]