Multimodal Image Synthesis and Editing: The Generative AI Era (2112.13592v6)

Abstract: As information exists in various modalities in real world, effective interaction and fusion among multimodal information plays a key role for the creation and perception of multimodal data in computer vision and deep learning research. With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years. Instead of providing explicit guidance for network training, multimodal guidance offers intuitive and flexible means for image synthesis and editing. On the other hand, this field is also facing several challenges in alignment of multimodal features, synthesis of high-resolution images, faithful evaluation metrics, etc. In this survey, we comprehensively contextualize the advance of the recent multimodal image synthesis and editing and formulate taxonomies according to data modalities and model types. We start with an introduction to different guidance modalities in image synthesis and editing, and then describe multimodal image synthesis and editing approaches extensively according to their model types. After that, we describe benchmark datasets and evaluation metrics as well as corresponding experimental results. Finally, we provide insights about the current research challenges and possible directions for future research. A project associated with this survey is available at https://github.com/fnzhan/Generative-AI.

• The paper introduces a comprehensive framework for multimodal image synthesis and editing using diverse generative AI methods.
• It compares generative frameworks such as GANs, diffusion models, transformers, and NeRFs, highlighting their strengths and challenges.
• It emphasizes the need for expansive multimodal datasets and efficient architectures to drive 3D-aware synthesis and practical applications.

Multimodal Image Synthesis and Editing: The Generative AI Era

The paper "Multimodal Image Synthesis and Editing: The Generative AI Era" offers a comprehensive analysis of the landscape of multimodal image synthesis and editing (MISE) leveraging generative AI. It addresses the different types of modalities, existing methods, datasets, evaluation metrics, and future directions in the field.

Key Modalities and Implementation Approaches

The paper categorizes the primary modalities in MISE, including visual guidance, text guidance, audio guidance, and other modalities such as scene graphs and brain signals. Visual guidance, including segmentation maps and sketches, offers spatial structure, facilitating precise image synthesis. Text guidance provides flexibility and wide-ranging expression, complementing visual inputs. Audio guidance introduces temporal dynamics essential for tasks such as talking-face generation, while novel modalities like scene graphs and brain signals present new frontiers for exploration in image synthesis.

Generative Frameworks

The exploration of generative methodologies, specifically GANs, diffusion models, autoregressive models, and neural radiance fields (NeRF), highlights the strengths and weaknesses of each approach.

• GAN-based Methods: While offering high-fidelity image generation, GANs face challenges in stable training and ensuring diversity. Conditional GANs incorporate multimodal guidance through techniques like SPADE and cross-attention, while GAN inversion techniques facilitate cross-modal image manipulation using pre-trained GANs.
• Diffusion-based Models: These models exhibit superior generative performance by leveraging a robust probabilistic framework. Techniques such as conditional incorporation and latent space regularization are discussed, alongside guidance function methods and model fine-tuning as effective strategies for image synthesis.
• Autoregressive Models: Utilizing Transformer architectures, these models support a unified handling of multimodal inputs. The paper discusses vector quantization for efficient data compression and bidirectional context modeling for enhanced generative performance.
• NeRF-based Methods: NeRFs represent the 3D geometry of scenes, enabling 3D-aware image synthesis. The paper covers both per-scene optimizations and generative NeRFs, emphasizing the potential of neural rendering in creating realistic imagery from limited data.

Datasets and Evaluation

The paper identifies key datasets and evaluation metrics essential for MISE. Metrics such as FID and LPIPS provide quantitative measures of image quality and diversity, while task-specific metrics evaluate alignment between synthesized images and conditions. A highlight is the call for more extensive multimodal datasets and faithful evaluation metrics, considering the increasing complexity of synthesis tasks.

Future Directions

Future research is encouraged to develop large-scale, multimodal datasets and efficient models that accommodate the complexities of real-world data distributions. There is also a push for 3D-aware synthesis, leveraging neural rendering to advance applications beyond traditional 2D paradigms. The challenges of slow inference speeds in autoregressive and diffusion models are noted, suggesting a need for architectural innovations that facilitate efficient generation.

Conclusion

The paper underscores significant advances and persistent challenges in MISE. While generative AI has empowered profound advancements in image synthesis, the field continues to evolve, pushing for models that offer increased fidelity, diversity, and adaptability to diverse multimodal inputs. This work is an invaluable resource for experienced researchers aiming to understand and contribute to this rapidly developing domain.