MV-Adapter: Multi-view Consistent Image Generation Made Easy
Zehuan Huang, Yuan-Chen Guo, Haoran Wang, Ran Yi, Lizhuang Ma, Yan-Pei Cao, Lu Sheng
2024-12-06
Summary
This paper talks about MV-Adapter, a new tool that makes it easier to generate consistent images from multiple viewpoints using existing text-to-image models without needing extensive changes.
What's the problem?
Current methods for generating multi-view images often require major changes to pre-trained models, which can be expensive and lead to lower image quality. This makes it hard for creators to efficiently produce high-quality images from different angles.
What's the solution?
The authors developed MV-Adapter, a plug-and-play solution that enhances existing text-to-image models without altering their core structure. By only updating certain parameters, MV-Adapter allows for efficient training and preserves the knowledge from the original models. It also includes innovative designs to better understand 3D shapes and integrates camera settings to improve image generation. This means users can easily create multi-view images with high quality and less effort.
Why it matters?
This research is important because it sets a new standard for generating multi-view images, making it more accessible for artists and developers. MV-Adapter's efficiency and flexibility could lead to more creative applications in fields like gaming, animation, and virtual reality, where consistent visuals from different angles are crucial.
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.