title: An Empirical Study of GPT-4o Image Generation Capabilities
summary: The landscape of image generation has rapidly evolved, from early GAN-based
  approaches to diffusion models and, most recently, to unified generative
  architectures that seek to bridge understanding and generation tasks. Recent
  advances, especially the GPT-4o, have demonstrated the feasibility of
  high-fidelity multimodal generation, their architectural design remains
  mysterious and unpublished. This prompts the question of whether image and text
  generation have already been successfully integrated into a unified framework
  for those methods. In this work, we conduct an empirical study of GPT-4o's
  image generation capabilities, benchmarking it against leading open-source and
  commercial models. Our evaluation covers four main categories, including
  text-to-image, image-to-image, image-to-3D, and image-to-X generation, with
  more than 20 tasks. Our analysis highlights the strengths and limitations of
  GPT-4o under various settings, and situates it within the broader evolution of
  generative modeling. Through this investigation, we identify promising
  directions for future unified generative models, emphasizing the role of
  architectural design and data scaling.
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paper_title: An Empirical Study of GPT-4o Image Generation Capabilities
paper_summary: The landscape of image generation has rapidly evolved, from early GAN-based
  approaches to diffusion models and, most recently, to unified generative
  architectures that seek to bridge understanding and generation tasks. Recent
  advances, especially the GPT-4o, have demonstrated the feasibility of
  high-fidelity multimodal generation, their architectural design remains
  mysterious and unpublished. This prompts the question of whether image and text
  generation have already been successfully integrated into a unified framework
  for those methods. In this work, we conduct an empirical study of GPT-4o's
  image generation capabilities, benchmarking it against leading open-source and
  commercial models. Our evaluation covers four main categories, including
  text-to-image, image-to-image, image-to-3D, and image-to-X generation, with
  more than 20 tasks. Our analysis highlights the strengths and limitations of
  GPT-4o under various settings, and situates it within the broader evolution of
  generative modeling. Through this investigation, we identify promising
  directions for future unified generative models, emphasizing the role of
  architectural design and data scaling.
paper_ai_summary: An empirical study of GPT-4o's image generation capabilities across multiple tasks reveals its strengths and limitations compared to other models, highlighting the importance of architectural design and data scaling in unified generative frameworks.
paper_ai_keywords: GAN diffusion models unified generative architectures text-to-image image-to-image image-to-3D image-to-X