Generating Multimodal Images with GAN: Integrating Text, Image, and Style

• URL: http://arxiv.org/abs/2501.02167v1
• Date: Sat, 04 Jan 2025 02:51:28 GMT
• Title: Generating Multimodal Images with GAN: Integrating Text, Image, and Style
• Authors: Chaoyi Tan, Wenqing Zhang, Zhen Qi, Kowei Shih, Xinshi Li, Ao Xiang,
• Abstract summary: We propose a multimodal image generation method based on Generative Adversarial Networks (GAN) This method involves the design of a text encoder, an image feature extractor, and a style integration module. Experimental results show that our method produces images with high clarity and consistency across multiple public datasets.
• Score: 7.481665175881685
• License:
• Abstract: In the field of computer vision, multimodal image generation has become a research hotspot, especially the task of integrating text, image, and style. In this study, we propose a multimodal image generation method based on Generative Adversarial Networks (GAN), capable of effectively combining text descriptions, reference images, and style information to generate images that meet multimodal requirements. This method involves the design of a text encoder, an image feature extractor, and a style integration module, ensuring that the generated images maintain high quality in terms of visual content and style consistency. We also introduce multiple loss functions, including adversarial loss, text-image consistency loss, and style matching loss, to optimize the generation process. Experimental results show that our method produces images with high clarity and consistency across multiple public datasets, demonstrating significant performance improvements compared to existing methods. The outcomes of this study provide new insights into multimodal image generation and present broad application prospects.

Related papers

• UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation [64.8341372591993]
  We propose a new approach to unify controllable generation within a single framework. Specifically, we propose the unified image-instruction adapter (UNIC-Adapter) built on the Multi-Modal-Diffusion Transformer architecture. Our UNIC-Adapter effectively extracts multi-modal instruction information by incorporating both conditional images and task instructions.
  arXiv  Detail & Related papers  (2024-12-25T15:19:02Z)
• Many-to-many Image Generation with Auto-regressive Diffusion Models [59.5041405824704]
  This paper introduces a domain-general framework for many-to-many image generation, capable of producing interrelated image series from a given set of images. We present MIS, a novel large-scale multi-image dataset, containing 12M synthetic multi-image samples, each with 25 interconnected images. We learn M2M, an autoregressive model for many-to-many generation, where each image is modeled within a diffusion framework.
  arXiv  Detail & Related papers  (2024-04-03T23:20:40Z)
• UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion [36.06457895469353]
  UNIMO-G is a conditional diffusion framework that operates on multimodal prompts with interleaved textual and visual inputs. It excels in both text-to-image generation and zero-shot subject-driven synthesis.
  arXiv  Detail & Related papers  (2024-01-24T11:36:44Z)
• ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors [105.37795139586075]
  We propose a new task for stylizing'' text-to-image models, namely text-driven stylized image generation. We present a new diffusion model (ControlStyle) via upgrading a pre-trained text-to-image model with a trainable modulation network. Experiments demonstrate the effectiveness of our ControlStyle in producing more visually pleasing and artistic results.
  arXiv  Detail & Related papers  (2023-11-09T15:50:52Z)
• Generating Images with Multimodal Language Models [78.6660334861137]
  We propose a method to fuse frozen text-only large language models with pre-trained image encoder and decoder models. Our model demonstrates a wide suite of multimodal capabilities: image retrieval, novel image generation, and multimodal dialogue.
  arXiv  Detail & Related papers  (2023-05-26T19:22:03Z)
• Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation [63.061871048769596]
  We present a novel Unified Multi-Modal Latent Diffusion (UMM-Diffusion) which takes joint texts and images containing specified subjects as input sequences. To be more specific, both input texts and images are encoded into one unified multi-modal latent space. Our method is able to generate high-quality images with complex semantics from both aspects of input texts and images.
  arXiv  Detail & Related papers  (2023-03-16T13:50:20Z)
• Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation [10.39028769374367]
  We present a new framework that takes text-to-image synthesis to the realm of image-to-image translation. Our method harnesses the power of a pre-trained text-to-image diffusion model to generate a new image that complies with the target text.
  arXiv  Detail & Related papers  (2022-11-22T20:39:18Z)
• Text-to-Image Generation via Implicit Visual Guidance and Hypernetwork [38.55086153299993]
  We develop an approach for text-to-image generation that embraces additional retrieval images, driven by a combination of implicit visual guidance loss and generative objectives. We propose a novel hypernetwork modulated visual-text encoding scheme to predict the weight update of the encoding layer. Experimental results show that our model guided with additional retrieval visual data outperforms existing GAN-based models.
  arXiv  Detail & Related papers  (2022-08-17T19:25:00Z)
• TediGAN: Text-Guided Diverse Face Image Generation and Manipulation [52.83401421019309]
  TediGAN is a framework for multi-modal image generation and manipulation with textual descriptions. StyleGAN inversion module maps real images to the latent space of a well-trained StyleGAN. visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. instance-level optimization is for identity preservation in manipulation.
  arXiv  Detail & Related papers  (2020-12-06T16:20:19Z)
• Multi-Image Summarization: Textual Summary from a Set of Cohesive Images [17.688344968462275]
  This paper proposes the new task of multi-image summarization. It aims to generate a concise and descriptive textual summary given a coherent set of input images. A dense average image feature aggregation network allows the model to focus on a coherent subset of attributes.
  arXiv  Detail & Related papers  (2020-06-15T18:45:35Z)

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.