  Point-interactive image colorization aims to colorize grayscale images when a
user provides the colors for specific locations. It is essential for
point-interactive colorization methods to appropriately propagate user-provided
colors (i.e., user hints) in the entire image to obtain a reasonably colorized
image with minimal user effort. However, existing approaches often produce
partially colorized results due to the inefficient design of stacking
convolutional layers to propagate hints to distant relevant regions. To address
this problem, we present iColoriT, a novel point-interactive colorization
Vision Transformer capable of propagating user hints to relevant regions,
leveraging the global receptive field of Transformers. The self-attention
mechanism of Transformers enables iColoriT to selectively colorize relevant
regions with only a few local hints. Our approach colorizes images in real-time
by utilizing pixel shuffling, an efficient upsampling technique that replaces
the decoder architecture. Also, in order to mitigate the artifacts caused by
pixel shuffling with large upsampling ratios, we present the local stabilizing
layer. Extensive quantitative and qualitative results demonstrate that our
approach highly outperforms existing methods for point-interactive
colorization, producing accurately colorized images with a user's minimal
effort. Official codes are available at
https://pmh9960.github.io/research/iColoriT
