| Language-agnostic representation learning of source code from structure and context D Zügner, T Kirschstein, M Catasta, J Leskovec, S Günnemann arXiv preprint arXiv:2103.11318, 2021 | 130 | 2021 |
| End-to-end learning for dimensional emotion recognition from physiological signals G Keren, T Kirschstein, E Marchi, F Ringeval, B Schuller 2017 IEEE International Conference on Multimedia and Expo (ICME), 985-990, 2017 | 58 | 2017 |
| NeRSemble: Multi-View Radiance Field Reconstruction of Human Heads T Kirschstein, S Qian, S Giebenhain, T Walter, M Nießner ACM Transactions on Graphics (TOG) 42 (4), 161:1-14, 2023 | 26 | 2023 |
| Learning Neural Parametric Head Models S Giebenhain, T Kirschstein, M Georgopoulos, M Rünz, L Agapito, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 22 | 2023 |
| GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians S Qian, T Kirschstein, L Schoneveld, D Davoli, S Giebenhain, M Nießner arXiv preprint arXiv:2312.02069, 2023 | 19 | 2023 |
| DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars T Kirschstein, S Giebenhain, M Nießner arXiv preprint arXiv:2311.18635, 2023 | 2 | 2023 |
| MonoNPHM: Dynamic Head Reconstruction from Monocular Videos S Giebenhain, T Kirschstein, M Georgopoulos, M Rünz, L Agapito, ... arXiv preprint arXiv:2312.06740, 2023 | | 2023 |
| Supplementary Material: Learning Neural Parametric Head Models S Giebenhain, T Kirschstein, M Georgopoulos, M Rünz, L Agapito, ... | | |
| TUM Data Innovation Lab J Beck, LM Bernhardt, H Chauhan, T Kirschstein, M Maier-Borst, ... | | |
