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Found 10 papers, 1 papers with code
Erasing Appearance Preservation in Optimization-based Smoothing
We show that partially ""erasing"" the appearance preservation facilitate adequate image smoothing.
Sketch2Manga: Shaded Manga Screening from Sketch with Diffusion Models
Unfortunately, there is no existing method that tailors for automatic manga screening, probably due to the difficulty of generating high-quality shaded high-frequency screentones.
Instance-guided Cartoon Editing with a Large-scale Dataset
Cartoon editing, appreciated by both professional illustrators and hobbyists, allows extensive creative freedom and the development of original narratives within the cartoon domain.
Highly Detailed and Temporal Consistent Video Stylization via Synchronized Multi-Frame Diffusion
However, they struggle to generate videos with both highly detailed appearance and temporal consistency.
Manga Rescreening with Interpretable Screentone Representation
To overcome these limitations, we propose a novel interpretable representation of screentones that disentangles their intensity and type features, enabling better recognition and synthesis of screentones.
Video Colorization with Pre-trained Text-to-Image Diffusion Models
In this paper, we present ColorDiffuser, an adaptation of a pre-trained text-to-image latent diffusion model for video colorization.
Improved Diffusion-based Image Colorization via Piggybacked Models
Our key idea is to exploit the color prior knowledge in the pre-trained T2I diffusion model for realistic and diverse colorization.
Neural Recognition of Dashed Curves With Gestalt Law of Continuity
While humans can intuitively recognize dashed curves from disjoint curve segments based on the law of continuity in Gestalt psychology, it is extremely difficult for computers to model the Gestalt law of continuity and recognize the dashed curves since high-level semantic understanding is needed for this task.
Invertible Tone Mapping with Selectable Styles
In this paper, we propose an invertible tone mapping method that converts the multi-exposure HDR to a true LDR (8-bit per color channel) and reserves the capability to accurately restore the original HDR from this {\em invertible LDR}.
User-Guided Line Art Flat Filling With Split Filling Mechanism
We present a deep learning framework for user-guided line art flat filling that can compute the "influence areas" of the user color scribbles, i. e., the areas where the user scribbles should propagate and influence.
