While text-to-3D and image-to-3D generation tasks have received considerable
attention, one important but under-explored field between them is controllable
text-to-3D generation, which we mainly focus on in this work. To address this
task, 1) we introduce Multi-view ControlNet (MVControl), a novel neural network
architecture designed to enhance existing pre-trained multi-view diffusion
models by integrating additional input conditions, such as edge, depth, normal,
and scribble maps. Our innovation lies in the introduction of a conditioning
module that controls the base diffusion model using both local and global
embeddings, which are computed from the input condition images and camera
poses. Once trained, MVControl is able to offer 3D diffusion guidance for
optimization-based 3D generation. And, 2) we propose an efficient multi-stage
3D generation pipeline that leverages the benefits of recent large
reconstruction models and score distillation algorithm. Building upon our
MVControl architecture, we employ a unique hybrid diffusion guidance method to
direct the optimization process. In pursuit of efficiency, we adopt 3D
Gaussians as our representation instead of the commonly used implicit
representations. We also pioneer the use of SuGaR, a hybrid representation that
binds Gaussians to mesh triangle faces. This approach alleviates the issue of
poor geometry in 3D Gaussians and enables the direct sculpting of fine-grained
geometry on the mesh. Extensive experiments demonstrate that our method
achieves robust generalization and enables the controllable generation of
high-quality 3D content.

本研究主要关注在控制性文本生成 3D 场景方面的多视图控制网络架构，该网络结合额外的输入条件，如边缘、深度、法线和素描图，使用局部和全局嵌入来控制基础扩散模型，并通过优化实现 3D 生成，最终实现了高质量、可控的 3D 内容的生成。

可控的文本到三维生成：面对齐的高斯扩散

Controllable Text-to-3D Generation via Surface-Aligned Gaussian  Splatting

Recent advances in 3D content creation mostly leverage optimization-based 3D
generation via score distillation sampling (SDS). Though promising results have
been exhibited, these methods often suffer from slow per-sample optimization,
limiting their practical usage. In this paper, we propose DreamGaussian, a
novel 3D content generation framework that achieves both efficiency and quality
simultaneously. Our key insight is to design a generative 3D Gaussian Splatting
model with companioned mesh extraction and texture refinement in UV space. In
contrast to the occupancy pruning used in Neural Radiance Fields, we
demonstrate that the progressive densification of 3D Gaussians converges
significantly faster for 3D generative tasks. To further enhance the texture
quality and facilitate downstream applications, we introduce an efficient
algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning
stage to refine the details. Extensive experiments demonstrate the superior
efficiency and competitive generation quality of our proposed approach.
Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes
from a single-view image, achieving approximately 10 times acceleration
compared to existing methods.

最近在 3D 内容创建方面的最新进展主要利用基于优化的 3D 生成通过得分蒸馏抽样（SDS）。尽管取得了有希望的结果，但这些方法往往受到每个样本优化速度慢的限制，限制了它们的实际应用。本文提出了 DreamGaussian，一个同时实现效率和质量的创新性 3D 内容生成框架。我们的关键见解是在 UV 空间中设计了一个具有伴随网格提取和纹理细化功能的生成性 3D 高斯飞溅模型。与神经辐射场中使用的占位修剪不同，我们证明了 3D 高斯曲线的渐进致密化对于 3D 生成任务的收敛速度更快。为了进一步提高纹理质量并促进下游应用，我们引入了一种将 3D 高斯曲线转换为纹理网格的高效算法，并应用了一个微调阶段来细化细节。大量实验证明了我们提出方法的卓越效率和竞争的生成质量。值得注意的是，DreamGaussian 从单视图图像中仅用 2 分钟即可生成高质量的纹理网格，与现有方法相比加速了大约 10 倍。