GenColor: Generative Color-Concept Association in Visual Design

• URL: http://arxiv.org/abs/2503.03236v1
• Date: Wed, 05 Mar 2025 07:29:12 GMT
• Title: GenColor: Generative Color-Concept Association in Visual Design
• Authors: Yihan Hou, Xingchen Zeng, Yusong Wang, Manling Yang, Xiaojiao Chen, Wei Zeng,
• Abstract summary: We introduce a generative approach for mining semantically resonant colors leveraging images generated by text-to-image models.<n>The framework comprises three stages: concept instancing produces generative samples using diffusion models, text-guided image segmentation identifies concept-relevant regions within the image, and color association extracts primarily accompanied by accent colors.
• Score: 1.9642328328398817
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing approaches for color-concept association typically rely on query-based image referencing, and color extraction from image references. However, these approaches are effective only for common concepts, and are vulnerable to unstable image referencing and varying image conditions. Our formative study with designers underscores the need for primary-accent color compositions and context-dependent colors (e.g., 'clear' vs. 'polluted' sky) in design. In response, we introduce a generative approach for mining semantically resonant colors leveraging images generated by text-to-image models. Our insight is that contemporary text-to-image models can resemble visual patterns from large-scale real-world data. The framework comprises three stages: concept instancing produces generative samples using diffusion models, text-guided image segmentation identifies concept-relevant regions within the image, and color association extracts primarily accompanied by accent colors. Quantitative comparisons with expert designs validate our approach's effectiveness, and we demonstrate the applicability through cases in various design scenarios and a gallery.

Related papers

• Color Conditional Generation with Sliced Wasserstein Guidance [44.99833362998488]
  SW-Guidance is a training-free approach for image generation conditioned on the color distribution of a reference image. Our method outperforms state-of-the-art techniques for color-conditional generation in terms of color similarity to the reference.
  arXiv  Detail & Related papers  (2025-03-24T18:06:03Z)
• ColorFlow: Retrieval-Augmented Image Sequence Colorization [65.93834649502898]
  We propose a three-stage diffusion-based framework tailored for image sequence colorization in industrial applications. Unlike existing methods that require per-ID finetuning or explicit ID embedding extraction, we propose a novel Retrieval Augmented Colorization pipeline. Our pipeline also features a dual-branch design: one branch for color identity extraction and the other for colorization.
  arXiv  Detail & Related papers  (2024-12-16T14:32:49Z)
• MultiColor: Image Colorization by Learning from Multiple Color Spaces [4.738828630428634]
  MultiColor is a new learning-based approach to automatically colorize grayscale images. We employ a set of dedicated colorization modules for individual color space. With these predicted color channels representing various color spaces, a complementary network is designed to exploit the complementarity and generate pleasing and reasonable colorized images.
  arXiv  Detail & Related papers  (2024-08-08T02:34:41Z)
• Palette-based Color Transfer between Images [9.471264982229508]
  We propose a new palette-based color transfer method that can automatically generate a new color scheme. With a redesigned palette-based clustering method, pixels can be classified into different segments according to color distribution. Our method exhibits significant advantages over peer methods in terms of natural realism, color consistency, generality, and robustness.
  arXiv  Detail & Related papers  (2024-05-14T01:41:19Z)
• Automatic Controllable Colorization via Imagination [55.489416987587305]
  We propose a framework for automatic colorization that allows for iterative editing and modifications. By understanding the content within a grayscale image, we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring, mimicking the process of human experts.
  arXiv  Detail & Related papers  (2024-04-08T16:46:07Z)
• Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization [56.12990759116612]
  Pick-and-Draw is a training-free semantic guidance approach to boost identity consistency and generative diversity for personalization methods. The proposed approach can be applied to any personalized diffusion models and requires as few as a single reference image.
  arXiv  Detail & Related papers  (2024-01-30T05:56:12Z)
• ColorizeDiffusion: Adjustable Sketch Colorization with Reference Image and Text [5.675944597452309]
  We introduce two variations of an image-guided latent diffusion model utilizing different image tokens from the pre-trained CLIP image encoder. We propose corresponding manipulation methods to adjust their results sequentially using weighted text inputs.
  arXiv  Detail & Related papers  (2024-01-02T22:46:12Z)
• Language-based Photo Color Adjustment for Graphic Designs [38.43984897069872]
  We introduce an interactive language-based approach for photo recoloring. Our model can predict the source colors and the target regions, and then recolor the target regions with the source colors based on the given language-based instruction.
  arXiv  Detail & Related papers  (2023-08-06T08:53:49Z)
• Unsupervised Compositional Concepts Discovery with Text-to-Image Generative Models [80.75258849913574]
  In this paper, we consider the inverse problem -- given a collection of different images, can we discover the generative concepts that represent each image? We present an unsupervised approach to discover generative concepts from a collection of images, disentangling different art styles in paintings, objects, and lighting from kitchen scenes, and discovering image classes given ImageNet images.
  arXiv  Detail & Related papers  (2023-06-08T17:02:15Z)
• PalGAN: Image Colorization with Palette Generative Adversarial Networks [51.59276436217957]
  We propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances.
  arXiv  Detail & Related papers  (2022-10-20T12:28:31Z)
• Deep Line Art Video Colorization with a Few References [49.7139016311314]
  We propose a deep architecture to automatically color line art videos with the same color style as the given reference images. Our framework consists of a color transform network and a temporal constraint network. Our model can achieve even better coloring results by fine-tuning the parameters with only a small amount of samples.
  arXiv  Detail & Related papers  (2020-03-24T06:57:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.