Then we introduce a semantic mapper that produces the proxy masks from various input conditions including semantic masks.

This process enables the value (feature maps) to be rearranged (through a weighted sum) to align with the form of the query, thereby reflecting their strong correspondence. The input noise is removed because its stochasticity slows down the training. Given the need for balancing between high correspondence and image quality, we empirically set the weights of our loss terms. To demonstrate the influence of the additional losses introduced in our method, we provide both quantitative and qualitative ablations in Figure S2 and S3, respectively. Nonetheless, caution is warranted when overly increasing the number of clusters.

Semantic image synthesis (SIS) aims to generate realistic images according to semantic masks given by a user. Although recent methods produce high quality results with fine spatial control, SIS requires expensive pixel-level annotation of the training images. On the other hand, manipulating intermediate feature maps in a pretrained unconditional generator such as StyleGAN supports coarse spatial control without heavy annotation. In this paper, we introduce a new approach, for reflecting user's detailed guiding masks on a pretrained unconditional generator. Our method converts a user's guiding mask to a proxy mask through a semantic mapper.

This process enables the value (feature maps) to be rearranged (through a weighted sum) to align with the form of the query, thereby reflecting their strong correspondence. The input noise is removed because its stochasticity slows down the training. Given the need for balancing between high correspondence and image quality, we empirically set the weights of our loss terms. To demonstrate the influence of the additional losses introduced in our method, we provide both quantitative and qualitative ablations in Figure S2 and S3, respectively. Nonetheless, caution is warranted when overly increasing the number of clusters.

Semantic image synthesis (SIS) aims to generate realistic images according to semantic masks given by a user. Although recent methods produce high quality results with fine spatial control, SIS requires expensive pixel-level annotation of the training images. On the other hand, manipulating intermediate feature maps in a pretrained unconditional generator such as StyleGAN supports coarse spatial control without heavy annotation. In this paper, we introduce a new approach, for reflecting user's detailed guiding masks on a pretrained unconditional generator. Our method converts a user's guiding mask to a proxy mask through a semantic mapper.

We present an approach for the analysis of hybrid visual compositions in animation in the domain of ephemeral film. We combine ideas from semi-supervised and weakly supervised learning to train a model that can segment hybrid compositions without requiring pre-labeled segmentation masks. We evaluate our approach on a set of ephemeral films from 13 film archives. Results demonstrate that the proposed learning strategy yields a performance close to a fully supervised baseline. On a qualitative level the performed analysis provides interesting insights on hybrid compositions in animation film.