MV-Adapter: Multi-view Consistent Image Generation Made Easy

ICLR 2025 Conference Submission719 Authors

Keywords: Multi-view Image Generation, 3D Generation, Diffusion Models

TL;DR: MV-Adapter is a versatile plug-and-play adapter that enhances T2I models and their derivatives for multi-view generation under various conditions, which helps in 3D generation, 3D texture generation and other applications.

Abstract: Generating multi-view images of an object has important applications in content creation and perception. Existing methods achieved this by making invasive changes to pre-trained text-to-image (T2I) models and performing full-parameter training, leading to three main limitations: (1) High computational costs, especially for high-resolution outputs; (2) Incompatibility with derivatives and extensions of the base model, such as personalized models, distilled few-step models, and plugins like ControlNets; (3) Limited versatility, as they primarily serve a single purpose and cannot handle diverse conditioning signals such as text, images, and geometry. In this paper, we present MV-Adapter to address all the above limitations. MV-Adapter is designed to be a plug-and-play module working on top of pre-trained T2I models. This enables efficient training for high-resolution synthesis while maintaining full compatibility with all kinds of derivatives of the base T2I model. MV-Adapter provides a unified implementation for generating multi-view images from various conditions, facilitating applications such as text- and image-based 3D generation and texturing. We demonstrate that MV-Adapter sets a new quality standard for multi-view image generation, and opens up new possibilities due to its adaptability and versatility.

Primary Area: generative models

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Submission Number: 719