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

Algorithms to solve variational regularization of ill-posed inverse problems
usually involve operators that depend on a collection of continuous parameters.
When these operators enjoy some (local) regularity, these parameters can be
selected using the so-called Stein Unbiased Risk Estimate (SURE). While this
selection is usually performed by exhaustive search, we address in this work
the problem of using the SURE to efficiently optimize for a collection of
continuous parameters of the model. When considering non-smooth regularizers,
such as the popular l1-norm corresponding to soft-thresholding mapping, the
SURE is a discontinuous function of the parameters preventing the use of
gradient descent optimization techniques. Instead, we focus on an approximation
of the SURE based on finite differences as proposed in (Ramani et al., 2008).
Under mild assumptions on the estimation mapping, we show that this
approximation is a weakly differentiable function of the parameters and its
weak gradient, coined the Stein Unbiased GrAdient estimator of the Risk
(SUGAR), provides an asymptotically (with respect to the data dimension)
unbiased estimate of the gradient of the risk. Moreover, in the particular case
of soft-thresholding, it is proved to be also a consistent estimator. This
gradient estimate can then be used as a basis to perform a quasi-Newton
optimization. The computation of the SUGAR relies on the closed-form (weak)
differentiation of the non-smooth function. We provide its expression for a
large class of iterative methods including proximal splitting ones and apply
our strategy to regularizations involving non-smooth convex structured
penalties. Illustrations on various image restoration and matrix completion
problems are given.

本文提出了一种使用 Stein 无偏风险估计（SURE）和弱梯度（SUGAR）来优化模型连续参数集合的方法，旨在解决使用非平滑正则化（如软阈值）时，SURE 成为参数的不连续函数而不能使用渐变下降优化技术的问题。同时，本文还给出了 SUGAR 应用于正则化问题的实例研究。

多参数选择的 Stein 无偏梯度估计器（SUGAR）风险

Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple  parameter selection

Encoding finite linear CSPs as Boolean formulas and solving them by using
modern SAT solvers has proven to be highly effective, as exemplified by the
award-winning sugar system. We here develop an alternative approach based on
ASP. This allows us to use first-order encodings providing us with a high
degree of flexibility for easy experimentation with different implementations.
The resulting system aspartame re-uses parts of sugar for parsing and
normalizing CSPs. The obtained set of facts is then combined with an ASP
encoding that can be grounded and solved by off-the-shelf ASP systems. We
establish the competitiveness of our approach by empirically contrasting
aspartame and sugar.

通过 ASP，开发了一种替代的基于一阶编码的方法，提供了高度的灵活性来轻松尝试不同的实现，并将结果与基于布尔公式和现代 SAT 求解器的先前方法进行了比较。