We introduce ShowRoom3D, a three-stage approach for generating high-quality
3D room-scale scenes from texts. Previous methods using 2D diffusion priors to
optimize neural radiance fields for generating room-scale scenes have shown
unsatisfactory quality. This is primarily attributed to the limitations of 2D
priors lacking 3D awareness and constraints in the training methodology. In
this paper, we utilize a 3D diffusion prior, MVDiffusion, to optimize the 3D
room-scale scene. Our contributions are in two aspects. Firstly, we propose a
progressive view selection process to optimize NeRF. This involves dividing the
training process into three stages, gradually expanding the camera sampling
scope. Secondly, we propose the pose transformation method in the second stage.
It will ensure MVDiffusion provide the accurate view guidance. As a result,
ShowRoom3D enables the generation of rooms with improved structural integrity,
enhanced clarity from any view, reduced content repetition, and higher
consistency across different perspectives. Extensive experiments demonstrate
that our method, significantly outperforms state-of-the-art approaches by a
large margin in terms of user study.

我们介绍了 ShowRoom3D，这是一种从文本生成高质量的 3D 房间场景的三阶段方法。我们利用 3D 扩散先验 MVDiffusion 优化 3D 房间场景，并通过逐步扩展相机采样范围的渐进式视角选择和第二阶段的姿态变换方法，实现改善结构完整性、从任意视角提高清晰度、减少内容重复性和不同视角的一致性，显著超越了现有方法。

ShowRoom3D: 使用三维先验生成高质量的三维房间

ShowRoom3D: Text to High-Quality 3D Room Generation Using 3D Priors

This paper introduces MVDiffusion, a simple yet effective multi-view image
generation method for scenarios where pixel-to-pixel correspondences are
available, such as perspective crops from panorama or multi-view images given
geometry (depth maps and poses). Unlike prior models that rely on iterative
image warping and inpainting, MVDiffusion concurrently generates all images
with a global awareness, encompassing high resolution and rich content,
effectively addressing the error accumulation prevalent in preceding models.
MVDiffusion specifically incorporates a correspondence-aware attention
mechanism, enabling effective cross-view interaction. This mechanism underpins
three pivotal modules: 1) a generation module that produces low-resolution
images while maintaining global correspondence, 2) an interpolation module that
densifies spatial coverage between images, and 3) a super-resolution module
that upscales into high-resolution outputs. In terms of panoramic imagery,
MVDiffusion can generate high-resolution photorealistic images up to
1024$\times$1024 pixels. For geometry-conditioned multi-view image generation,
MVDiffusion demonstrates the first method capable of generating a textured map
of a scene mesh. The project page is at this https URL

MVDiffusion 是一种基于视角差异的、并行、全局相互作用的图片生成模型，其特征是应用了一个各视图之间基于对应关系的注意力机制，在保持全局一致的前提下，同时生成所有图片，可用于全景图像和基于几何的多视图图像生成。