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Many-to-Many Image Generation with Auto-regressive Diffusion Models


Core Concepts
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, offering a scalable solution that obviates the need for task-specific solutions across different multi-image scenarios.
Abstract
The paper presents a novel framework for multi-image to multi-image generation, called Many-to-many Diffusion (M2M), which can perceive and generate an arbitrary number of interrelated images in an auto-regressive manner. Key highlights: The authors introduce MIS, a large-scale multi-image dataset containing 12M synthetic multi-image samples, each with 25 interconnected images. This dataset is used to train the M2M model. M2M explores two main model variants: M2M with Self-encoder (M2M-Self) and M2M with DINO encoder (M2M-DINO). These models differ in how they encode the preceding images. The core component of M2M is the Image-Set Attention module, which enables the model to learn and understand the intricate interconnections within a set of images, facilitating more contextually coherent multi-image generation. Experiments show that M2M can effectively capture style and content from preceding images and generate novel images following the observed patterns. It also exhibits zero-shot generalization to real images. Through task-specific fine-tuning, M2M demonstrates adaptability to various multi-image generation tasks, including Novel View Synthesis and Visual Procedure Generation.
Stats
"Recent advancements in image generation have made significant progress, yet existing models present limitations in perceiving and generating an arbitrary number of interrelated images within a broad context." "MIS consists of a total of 12M synthetic multi-image set samples, each containing 25 interconnected images." "Stable Diffusion generates images conditioned on CLIP (Radford et al., 2021) text embeddings."
Quotes
"This paper underscores the need for a holistic exploration into the general-domain multi-image to multi-image generation paradigm, where models are designed to perceive and generate an arbitrary number of interrelated images within a broader context." "Leveraging our MIS dataset, we propose Many-to-many Diffusion (M2M), a conditional diffusion model that can perceive and generate an arbitrary number of interrelated images in an auto-regressive manner." "Impressively, despite being trained solely on synthetic data, our model exhibits zero-shot generalization to real images."

Key Insights Distilled From

by Ying Shen,Yi... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03109.pdf
Many-to-many Image Generation with Auto-regressive Diffusion Models

Deeper Inquiries

How can the proposed M2M framework be extended to handle more complex multi-image scenarios, such as video generation or 3D object generation

The proposed M2M framework can be extended to handle more complex multi-image scenarios by incorporating additional components and mechanisms tailored to the specific requirements of tasks like video generation or 3D object generation. For video generation, the framework can be adapted to process sequential frames of images, enabling the generation of coherent video sequences. This adaptation may involve incorporating temporal information processing modules, such as recurrent neural networks or transformers, to capture the temporal dependencies between frames. Additionally, techniques like motion estimation and interpolation can be integrated to enhance the realism and smoothness of the generated videos. In the context of 3D object generation, the M2M framework can be extended to leverage 3D representations and structures. By incorporating 3D convolutional layers or point cloud processing modules, the model can learn to generate multi-view images of 3D objects from different perspectives. Techniques like mesh generation and rendering can also be integrated to enable the generation of realistic 3D object representations. Furthermore, the framework can benefit from incorporating domain-specific knowledge about 3D object properties and structures to improve the accuracy and fidelity of the generated images.

What are the potential limitations of the current MIS dataset, and how could it be further improved to better capture the diversity and complexity of real-world multi-image scenarios

The current MIS dataset, while comprehensive, may have potential limitations that could be addressed to better capture the diversity and complexity of real-world multi-image scenarios. Some potential limitations include: Semantic Diversity: The dataset may lack sufficient semantic diversity in the interconnections between images, leading to limited variations in the relationships between images within a set. To improve this, additional diverse semantic relationships can be introduced, covering a wider range of scenarios and contexts. Realism and Complexity: The synthetic nature of the dataset may limit its ability to capture the realism and complexity of real-world multi-image scenarios. To address this, incorporating real-world images or data augmentation techniques to introduce more realistic elements and variations can enhance the dataset's representational power. Scale and Variability: While the dataset contains a large number of samples, ensuring sufficient variability in image content, style, and context is crucial. Introducing more diverse image sources, styles, and contexts can enhance the dataset's variability and robustness. Improvements to the MIS dataset could involve: Data Augmentation: Introducing diverse transformations, such as rotations, translations, and color variations, to increase the dataset's variability. Real Data Integration: Incorporating real-world images or data to supplement the synthetic data and enhance the dataset's realism. Semantic Relationship Expansion: Including a wider range of semantic relationships and contexts to capture a more diverse set of multi-image scenarios.

Given the model's ability to generate images in an auto-regressive manner, how could this capability be leveraged to enable interactive or iterative multi-image generation, where users can provide feedback and guide the generation process

The model's ability to generate images in an auto-regressive manner can be leveraged to enable interactive or iterative multi-image generation by incorporating feedback mechanisms and user guidance. This interactive generation process can involve the following steps: User Feedback Integration: Allow users to provide feedback on generated images, such as liking or disliking specific aspects, to guide the generation process. Iterative Refinement: Incorporate user feedback into the model's training process to iteratively refine the generation process based on user preferences. Conditional Generation: Enable users to provide conditional cues or constraints to steer the generation process towards specific desired outcomes. Real-time Interaction: Implement a real-time feedback loop where users can interact with the model during the generation process, influencing the direction of image generation. By integrating user feedback and guidance mechanisms into the auto-regressive generation process, the model can adapt and refine its outputs based on user preferences, leading to more personalized and interactive multi-image generation experiences.
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