Name
Abstract | ||
|---|---|---|
Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator's layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.01124 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Image and video synthesis and generation | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Evangelos Ntavelis | 1 | 0 | 0.34 |
| Mohamad Shahbazi | 2 | 0 | 0.68 |
| Iason Kastanis | 3 | 1 | 0.68 |
| Radu Timofte | 4 | 1880 | 118.45 |
| Danelljan Martin | 5 | 1344 | 49.35 |
| Luc Van Gool | 6 | 0 | 0.34 |