GeoMVD：基于几何信息提取的几何增强多视图生成模型
GeoMVD: Geometry-Enhanced Multi-View Generation Model Based on Geometric Information Extraction
AI总结：
针对现有多视图生成方法跨视图一致性差、高分辨率生成计算难度高的问题，本文提出GeoMVD几何增强多视图扩散模型，通过几何信息提取、增强注意力等模块实现高质量一致多视图生成。
摘要：
多视图图像生成在计算机视觉领域，尤其是三维重建、虚拟现实、增强现实等场景中具备重要应用价值。现有多数依赖单图扩展的方法，在维持跨视图一致性、生成高分辨率输出方面面临显著的计算挑战。为解决上述问题，本文提出几何引导多视图扩散模型，该模型融合多视图几何信息提取机制与几何特征强度调节模块，可生成兼具跨视图一致性与丰富细节的图像。具体而言，本文设计多视图几何信息提取模块，利用深度图、法线图、前景分割掩码构建共享几何结构，保障不同视图间的形状与结构一致性；为提升生成过程中的一致性与细节还原效果，研发解耦式几何增强注意力机制，强化模型对关键几何细节的特征聚焦能力，进而提升整体图像质量与细节保留水平。此外，本文采用自适应学习策略对模型进行微调，使其更好地捕捉生成视图间的空间关系与视觉连贯性，保障结果的真实感；模型还引入迭代细化流程，通过多阶段图像生成逐步提升输出质量；最后提出动态几何信息强度调节机制，自适应调控几何数据的影响，在保障生成图像自然度的同时优化整体质量。更多细节可查看项目页面：https://sobeymil.github.io/GeoMVD.com。
英文摘要：
Multi-view image generation holds significant application value in computer vision, particularly in domains like 3D reconstruction, virtual reality, and augmented reality. Most existing methods, which rely on extending single images, face notable computational challenges in maintaining cross-view consistency and generating high-resolution outputs. To address these issues, we propose the Geometry-guided Multi-View Diffusion Model, which incorporates mechanisms for extracting multi-view geometric information and adjusting the intensity of geometric features to generate images that are both consistent across views and rich in detail. Specifically, we design a multi-view geometry information extraction module that leverages depth maps, normal maps, and foreground segmentation masks to construct a shared geometric structure, ensuring shape and structural consistency across different views. To enhance consistency and detail restoration during generation, we develop a decoupled geometry-enhanced attention mechanism that strengthens feature focus on key geometric details, thereby improving overall image quality and detail preservation. Furthermore, we apply an adaptive learning strategy that fine-tunes the model to better capture spatial relationships and visual coherence between the generated views, ensuring realistic results. Our model also incorporates an iterative refinement process that progressively improves the output quality through multiple stages of image generation. Finally, a dynamic geometry information intensity adjustment mechanism is proposed to adaptively regulate the influence of geometric data, optimizing overall quality while ensuring the naturalness of generated images. More details can be found on the project page: https://sobeymil.github.io/GeoMVD.com.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
