93 research outputs found
Taming Reversible Halftoning via Predictive Luminance
Traditional halftoning usually drops colors when dithering images with binary
dots, which makes it difficult to recover the original color information. We
proposed a novel halftoning technique that converts a color image into a binary
halftone with full restorability to its original version. Our novel base
halftoning technique consists of two convolutional neural networks (CNNs) to
produce the reversible halftone patterns, and a noise incentive block (NIB) to
mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the
conflicts between the blue-noise quality and restoration accuracy in our novel
base method, we proposed a predictor-embedded approach to offload predictable
information from the network, which in our case is the luminance information
resembling from the halftone pattern. Such an approach allows the network to
gain more flexibility to produce halftones with better blue-noise quality
without compromising the restoration quality. Detailed studies on the
multiple-stage training method and loss weightings have been conducted. We have
compared our predictor-embedded method and our novel method regarding spectrum
analysis on halftone, halftone accuracy, restoration accuracy, and the data
embedding studies. Our entropy evaluation evidences our halftone contains less
encoding information than our novel base method. The experiments show our
predictor-embedded method gains more flexibility to improve the blue-noise
quality of halftones and maintains a comparable restoration quality with a
higher tolerance for disturbances.Comment: to be published in IEEE Transactions on Visualization and Computer
Graphic
Text-Guided Texturing by Synchronized Multi-View Diffusion
This paper introduces a novel approach to synthesize texture to dress up a
given 3D object, given a text prompt. Based on the pretrained text-to-image
(T2I) diffusion model, existing methods usually employ a project-and-inpaint
approach, in which a view of the given object is first generated and warped to
another view for inpainting. But it tends to generate inconsistent texture due
to the asynchronous diffusion of multiple views. We believe such asynchronous
diffusion and insufficient information sharing among views are the root causes
of the inconsistent artifact. In this paper, we propose a synchronized
multi-view diffusion approach that allows the diffusion processes from
different views to reach a consensus of the generated content early in the
process, and hence ensures the texture consistency. To synchronize the
diffusion, we share the denoised content among different views in each
denoising step, specifically blending the latent content in the texture domain
from views with overlap. Our method demonstrates superior performance in
generating consistent, seamless, highly detailed textures, comparing to
state-of-the-art methods
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