MV-Adapter: An Efficient Approach for Multi-View Consistent Image Generation

Generating consistent images from multiple perspectives is a challenge in artificial intelligence. Traditional methods for multi-view image generation often require extensive adaptations to pre-trained text-to-image (T2I) models and full fine-tuning. This leads to high computational costs, especially with large base models and high-resolution images. Furthermore, image quality can be compromised due to optimization difficulties and the lack of high-quality 3D data. MV-Adapter offers a new approach to this problem.

How MV-Adapter Works

MV-Adapter is a versatile plug-and-play adapter that extends T2I models and their derivatives without changing the original network structure or feature space. By updating fewer parameters, MV-Adapter enables efficient training and preserves the knowledge embedded in pre-trained models, reducing the risk of overfitting. The adapter seamlessly integrates camera parameters and geometric information, enabling applications such as text- and image-based 3D generation and texturing.

The MV-Adapter consists of two main components:

1. A Condition Guider, which encodes camera or geometry conditions.

2. Decoupled Attention Layers, which include Multi-View Attention layers for learning multi-view consistency and optional Image Cross-Attention layers to support image-conditioned generation.

Advantages of MV-Adapter

MV-Adapter offers several advantages over conventional methods:

Efficiency: By updating fewer parameters, MV-Adapter significantly reduces computational costs and training time.

Adaptability: The adapter is compatible with various T2I models and their derivatives, including personalized models and distillation models.

Versatility: MV-Adapter supports various input conditions, including text, images, and geometry, enabling a wide range of applications.

Scalability: The adapter has been successfully demonstrated for multi-view generation with a resolution of 768x768 on Stable Diffusion XL (SDXL) and can be extended to arbitrary view generation.

Applications

MV-Adapter enables a variety of applications, including:

Text-to-Multiview Generation: Generating multiple views of an object based on a text description.

Image-to-Multiview Generation: Creating consistent views of an object starting from a single image.

Geometry-Guided Multiview Generation: Generating views while considering geometric information.

3D Generation and Texturing: Creating 3D models and textures from text or image inputs.

Conclusion

MV-Adapter represents a promising approach for efficient and versatile multi-view image generation. Due to its plug-and-play nature and ability to process various input conditions, MV-Adapter opens up new possibilities for applications in 3D modeling, content creation, and computer vision. The ability to generate high-resolution images and compatibility with various T2I models makes MV-Adapter a valuable tool for developers and researchers.

Bibliography: - https://huanngzh.github.io/MV-Adapter-Page/ - https://openreview.net/forum?id=kcmK2utDhu - https://github.com/huanngzh/MV-Adapter - https://openreview.net/pdf/297f74f39335bf82888e63b469b4298d7f141dc5.pdf - https://arxiv.org/abs/2410.18974 - https://github.com/huanngzh - https://arxiv.org/html/2410.18974v1 - https://www.reddit.com/r/ninjasaid13/ - https://huggingface.co/papers/2410.06985 - https://lakonik.github.io/mvedit/