image triangulation methods, which decompose an image into a series of triangles, are fundamental in artistic creation and imageprocessing. This paper introduces a novel framework that integrates cubic B & eacute...
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image triangulation methods, which decompose an image into a series of triangles, are fundamental in artistic creation and imageprocessing. This paper introduces a novel framework that integrates cubic B & eacute;zier curves into image triangulation, enabling the precise reconstruction of curved image features. Our developed framework constructs a well-structured curved triangle mesh, effectively preventing overlaps between curves. A refined energy function, grounded in differentiable rendering, establishes a direct link between mesh geometry and rendering effects and is instrumental in guiding the curved mesh generation. Additionally, we derive an explicit gradient formula with respect to mesh parameters, facilitating the adaptive and efficient optimization of these parameters to fully leverage the capabilities of cubic B & eacute;zier curves. Through experimental and comparative analyses with state-of-the-art methods, our approach demonstrates a significant enhancement in both numerical accuracy and visual quality.
In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality artistic faces with di...
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In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi-modal face stylization by leveraging the strengths of StyleGAN and integrates it into an encoder-decoder architecture. Specifically, we use the mid-resolution and high-resolution layers of StyleGAN as the decoder to generate high-quality faces, while aligning its low-resolution layer with the encoder to extract and preserve input facial details. We also introduce a two-stage training strategy, where we train the encoder in the first stage to align the feature maps with StyleGAN and enable a faithful reconstruction of input faces. In the second stage, the entire network is fine-tuned with artistic data for stylized face generation. To enable the fine-tuned model to be applied in zero-shot and one-shot stylization tasks, we train an additional mapping network from the large-scale Contrastive-Language-image-Pre-training (CLIP) space to a latent w+ space of fine-tuned StyleGAN. Qualitative and quantitative experiments show that our framework achieves superior performance in both one-shot and zero-shot face stylization tasks, outperforming state-of-the-art methods by a large margin.
Morphological operations are among the most popular classic image filters. The filter assumes the maximum or minimum value within a window and is often used for light object thickening and thinning operations, which a...
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Morphological operations are among the most popular classic image filters. The filter assumes the maximum or minimum value within a window and is often used for light object thickening and thinning operations, which are important components of various workflows, such as object recognition and stylization. Circular windows are preferred over rectangular windows for obtaining isotropic filter results. However, the existing efficient algorithms focus on rectangular or binary input images. Efficient morphological operations with circular windows for grayscale images remain challenging. In this study, we present a fast grayscale morphology heuristic computation algorithm that decomposes circular windows using the convex hull of circles. We significantly accelerate traditional methods based on Minkowski addition by introducing new decomposition rules specialized for circular windows. As our morphological operation using a convex hull can be computed independently for each pixel, the algorithm is efficient for modern multithreaded hardware.
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