Name
Title | ||
|---|---|---|
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images. |
Abstract | ||
|---|---|---|
We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-19769-7_30 | European Conference on Computer Vision |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengqi Li | 1 | 13 | 3.51 |
| Qianqian Wang | 2 | 132 | 26.59 |
| Noah Snavely | 3 | 4262 | 197.04 |
| Angjoo Kanazawa | 4 | 272 | 10.36 |