MV-Adapter: Multi-view Consistent Image Generation Made Easy (2412.03632v1)

Abstract: Existing multi-view image generation methods often make invasive modifications to pre-trained text-to-image (T2I) models and require full fine-tuning, leading to (1) high computational costs, especially with large base models and high-resolution images, and (2) degradation in image quality due to optimization difficulties and scarce high-quality 3D data. In this paper, we propose the first adapter-based solution for multi-view image generation, and introduce MV-Adapter, a versatile plug-and-play adapter that enhances T2I models and their derivatives without altering the original network structure or feature space. By updating fewer parameters, MV-Adapter enables efficient training and preserves the prior knowledge embedded in pre-trained models, mitigating overfitting risks. To efficiently model the 3D geometric knowledge within the adapter, we introduce innovative designs that include duplicated self-attention layers and parallel attention architecture, enabling the adapter to inherit the powerful priors of the pre-trained models to model the novel 3D knowledge. Moreover, we present a unified condition encoder that seamlessly integrates camera parameters and geometric information, facilitating applications such as text- and image-based 3D generation and texturing. MV-Adapter achieves multi-view generation at 768 resolution on Stable Diffusion XL (SDXL), and demonstrates adaptability and versatility. It can also be extended to arbitrary view generation, enabling broader applications. We demonstrate that MV-Adapter sets a new quality standard for multi-view image generation, and opens up new possibilities due to its efficiency, adaptability and versatility.

Insights into MV-Adapter: An Innovative Approach to Multi-View Image Generation

The paper "MV-Adapter: Multi-View Consistent Image Generation Made Easy" introduces a novel adapter-based approach designed to extend pre-trained text-to-image (T2I) diffusion models to support multi-view image generation tasks. The research addresses significant challenges related to the computational demands of current methodologies and their reliance on invasive modifications to the model's structure, all while maintaining image quality integrity. At its core, the MV-Adapter presents a plug-and-play solution which circumvents the need for full model fine-tuning by adopting an innovative mechanism of self-attention layer duplication, thereby efficiently modeling 3D geometric knowledge.

The MV-Adapter differentiates itself by integrating into pre-existing T2I models without disrupting their original structure and feature space. This approach enables it to update fewer parameters during training, thus mitigating risks of overfitting, which is a prevalent issue with traditional fine-tuning methods. The preservation of prior knowledge within the pre-trained models is critical, as it ensures that the models retain their robustness, especially when scaling to larger T2I models for high-resolution image generation.

A significant technical contribution of the paper is the introduction of a unified condition encoder that seamlessly combines camera parameters and geometric information, allowing for refined multi-view image generation. The MV-Adapter achieves efficient multi-view generation even at a resolution of 768 on Stable Diffusion XL, showcasing adaptability and versatility not previously observed in multi-view generation methods. This quality is notably important as existing advanced methods struggle with maintaining image resolution at higher levels, typically capping at 512, which this paper's approach effectively surpasses.

Empirical results presented within the research highlight MV-Adapter’s superior performance over existing methods in both text-to-multi and image-to-multi-view generation. The paper reports improvements with metrics like Inception Score (IS), CLIP Score, and Frechet Inception Distance (FID), pointing to significantly better image-text alignment and visual fidelity. Moreover, the MV-Adapter extends its functionality to arbitrary view generation, laying the groundwork for broader applications in fields such as 3D content creation and texture mapping.

The implications of adopting an adapter-based paradigm reach beyond immediate applications. It suggests a scalable methodology that can incorporate newer types of knowledge, potentially beyond canonical geometric understandings. For instance, the decoupled attention mechanism could inspire future developments in integrating physical or temporal knowledge into generative models. Moreover, MV-Adapter demonstrates the feasibility of incorporating detailed 3D knowledge efficiently into T2I models, igniting prospects for innovations in 3D scene generation and video-based applications.

In summary, the paper advances the state of multi-view image generation by presenting an approach that not only enhances efficiency but also ensures compatibility with existing T2I derivatives. The MV-Adapter sets a new quality benchmark by leveraging foundational model strengths, suggesting transformative prospects for integrating more complex and varied types of conditioning information in future artificial intelligence research. This contribution is poised to drive next-generation applications requiring consistent multi-view imaging, thus enhancing both theoretical and practical developments in AI-driven content creation.