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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Generalizable 4D NeRF |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | Representing 3 dimensional scenes as Neural Radiance Fields (NeRF) has shown impressive results for novel view synthesis. Generalizable and dynamic variations of NeRF have been studied extensively producing photorealistic results. However, a generalizable and dynamic NeRF remains a very challenging problem. An effective solution to this problem requires a large and diverse dataset portraying complex subject motion. In this work we provide an end-to-end framework for generating high-quality synthetic datasets with complex and realistic human motion tracked by multiple cameras moving along pseudo random trajectories as well as multiple static cameras. |
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