3D stylization, which entails the application of specific styles to
three-dimensional objects, holds significant commercial potential as it enables
the creation of diverse 3D objects with distinct moods and styles, tailored to
specific demands of different scenes. With recent advancements in text-driven
methods and artificial intelligence, the stylization process is increasingly
intuitive and automated, thereby diminishing the reliance on manual labor and
expertise. However, existing methods have predominantly focused on holistic
stylization, thereby leaving the application of styles to individual components
of a 3D object unexplored. In response, we introduce 3DStyleGLIP, a novel
framework specifically designed for text-driven, part-tailored 3D stylization.
Given a 3D mesh and a text prompt, 3DStyleGLIP leverages the vision-language
embedding space of the Grounded Language-Image Pre-training (GLIP) model to
localize the individual parts of the 3D mesh and modify their colors and local
geometries to align them with the desired styles specified in the text prompt.
3DStyleGLIP is effectively trained for 3D stylization tasks through a
part-level style loss working in GLIP's embedding space, supplemented by two
complementary learning techniques. Extensive experimental validation confirms
that our method achieves significant part-wise stylization capabilities,
demonstrating promising potential in advancing the field of 3D stylization.

介绍了一种基于文本驱动的、部分定制的 3D 风格化的新方法 3DStyleGLIP，该方法利用 Grounded Language-Image Pre-training (GLIP) 模型的视觉 - 语言嵌入空间来定位 3D 模型的各个部分，并按照文本提示中指定的风格修改它们的颜色和局部几何形状，通过在 GLIP 的嵌入空间中使用部分级风格损失和两种补充学习技术，有效地训练了 3DStyleGLIP，实验证实了该方法在部分级别的风格化任务上取得了显著成果，显示出推动 3D 风格化领域发展的潜力。

3DStyleGLIP：面向部分的文本引导的 3D 神经风格化

3DStyleGLIP: Part-Tailored Text-Guided 3D Neural Stylization

Current 3D stylization methods often assume static scenes, which violates the
dynamic nature of our real world. To address this limitation, we present
S-DyRF, a reference-based spatio-temporal stylization method for dynamic neural
radiance fields. However, stylizing dynamic 3D scenes is inherently challenging
due to the limited availability of stylized reference images along the temporal
axis. Our key insight lies in introducing additional temporal cues besides the
provided reference. To this end, we generate temporal pseudo-references from
the given stylized reference. These pseudo-references facilitate the
propagation of style information from the reference to the entire dynamic 3D
scene. For coarse style transfer, we enforce novel views and times to mimic the
style details present in pseudo-references at the feature level. To preserve
high-frequency details, we create a collection of stylized temporal pseudo-rays
from temporal pseudo-references. These pseudo-rays serve as detailed and
explicit stylization guidance for achieving fine style transfer. Experiments on
both synthetic and real-world datasets demonstrate that our method yields
plausible stylized results of space-time view synthesis on dynamic 3D scenes.

提出了 S-DyRF，一种基于参考的时空风格化方法，通过引入时间伪参考进行粗略和细致的风格转移，实现了动态 3D 场景的合理风格化结果

S-DyRF：基于参考的动态场景风格化辐射场

S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes

Recent progress in the text-driven 3D stylization of a single object has been
considerably promoted by CLIP-based methods. However, the stylization of
multi-object 3D scenes is still impeded in that the image-text pairs used for
pre-training CLIP mostly consist of an object. Meanwhile, the local details of
multiple objects may be susceptible to omission due to the existing supervision
manner primarily relying on coarse-grained contrast of image-text pairs. To
overcome these challenges, we present a novel framework, dubbed TeMO, to parse
multi-object 3D scenes and edit their styles under the contrast supervision at
multiple levels. We first propose a Decoupled Graph Attention (DGA) module to
distinguishably reinforce the features of 3D surface points. Particularly, a
cross-modal graph is constructed to align the object points accurately and noun
phrases decoupled from the 3D mesh and textual description. Then, we develop a
Cross-Grained Contrast (CGC) supervision system, where a fine-grained loss
between the words in the textual description and the randomly rendered images
are constructed to complement the coarse-grained loss. Extensive experiments
show that our method can synthesize high-quality stylized content and
outperform the existing methods over a wide range of multi-object 3D meshes.
Our code and results will be made publicly available

通过 TeMO 框架，解决了多对象 3D 场景的样式化问题，并利用交叉粗粒度对比监督系统构建细粒度损失，提高了多对象 3D 网格样式化的质量。

TeMO: 文本驱动的多物体网格三维风格化

TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes

We present a method for transferring the artistic features of an arbitrary
style image to a 3D scene. Previous methods that perform 3D stylization on
point clouds or meshes are sensitive to geometric reconstruction errors for
complex real-world scenes. Instead, we propose to stylize the more robust
radiance field representation. We find that the commonly used Gram matrix-based
loss tends to produce blurry results without faithful brushstrokes, and
introduce a nearest neighbor-based loss that is highly effective at capturing
style details while maintaining multi-view consistency. We also propose a novel
deferred back-propagation method to enable optimization of memory-intensive
radiance fields using style losses defined on full-resolution rendered images.
Our extensive evaluation demonstrates that our method outperforms baselines by
generating artistic appearance that more closely resembles the style image.
Please check our project page for video results and open-source
implementations: this https URL .

该论文提出了一种将任意风格图像的艺术特征传输到 3D 场景中的方法，采用了更为鲁棒的辐射场表示进行运算并通过最近邻法进行损失计算，同时采用一种新的延迟反向传播方法进行内存密集的辐射场优化，改善了以往的技术。