Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for
Fast Artistic Style Transfer
Abstract
Transferring artistic styles onto everyday photographs
has become an extremely popular task in both academia
and industry. Recently, offline training has replaced online iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic
style. This is because the transfer process fails to capture
small, intricate textures and maintain correct texture scales
of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful
representations of both color and luminance channels, and
performs stylization hierarchically with multiple losses of
increasing scales. Compared to state-of-the-art networks,
our network can also perform style transfer in nearly realtime by conducting much more sophisticated training of-
fline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not
just large-scale, obvious style cues but also subtle, exquisite
ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic
styles with color and texture cues at multiple scales.