LLMs Meet Multimodal Generation and Editing: An Overview
This project is a comprehensive survey and repository focused on exploring the role of Large Language Models (LLMs) in multimodal generation and editing. Multimodal encompasses various forms such as visual content (images, videos, and 3D graphics) and audio material (sound, speech, and music). The aim of the project is to curate an extensive list of research and advancements where LLMs intersect with multimodal content creation, providing a valuable resource for researchers and enthusiasts alike.
What Does the Repository Include?
The repository is divided into various sections that explore different aspects of multimodal generation and editing:
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Multimodal Generation: This section covers the creation of different forms of media using LLMs, with dedicated categories for image, video, and 3D generation as well as audio production.
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Multimodal Editing: Here, the focus is on editing capabilities in various modalities, again using the power of LLMs. This includes the manipulation and enhancement of images, videos, 3D models, and audio clips.
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Multimodal Agents and Understanding: Investigates how LLMs can be utilized to create intelligent agents capable of understanding and interpreting content across multiple modalities. This section extends into discussions around safety, ethical considerations, and functional applications of LLMs in complex multimodal environments.
Key Features and Research Highlights
Image Generation with LLMs
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InstantUnify: A unique integration of LLMs within diffusion models that aims to harmonize multimodal input for generating coherent and high-quality images.
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Commonsense-T2I Challenge: A study on the ability of text-to-image models to comprehend and implement commonsense reasoning, enhancing the realism and relevance of generated images.
Video and 3D Modalities
- Seamlessly transitions into video and 3D content generation, exploring how LLMs influence the creation and modification of intricate media forms.
Audio Generation
- Explores the generation of rich audio experiences through LLMs, spanning music and speech synthesis.
Community and Contribution
The project is open to contributions and welcomes insights from other researchers, offering opportunities to suggest improvements or include new findings via pull requests. This openness not only broadens the scope of research but also keeps the repository dynamic and up-to-date.
Tips for Research
- Researchers can search for papers by category, author, or specific tags like
customizationandtokenizer, creating an efficient pathway for discovering relevant literature in the multimodal domain.
Significance
This survey and repository serve as an invaluable resource for those researching or working with multimodal data and LLM technology. By collating and categorizing research findings, it simplifies access to the latest developments and underscores the potential of LLMs in advancing multimodal generation and editing tools. Whether for developing AI that better understands human communication or creating art and media, this project illustrates the rich tapestry of possibilities when language models meet complex, multimodal challenges.