Utilizing multi-view inputs to synthesize novel-view images, Neural Radiance
Fields (NeRF) have emerged as a popular research topic in 3D vision. In this
work, we introduce a Generalizable Semantic Neural Radiance Field (GSNeRF),
which uniquely takes image semantics into the synthesis process so that both
novel view images and the associated semantic maps can be produced for unseen
scenes. Our GSNeRF is composed of two stages: Semantic Geo-Reasoning and
Depth-Guided Visual rendering. The former is able to observe multi-view image
inputs to extract semantic and geometry features from a scene. Guided by the
resulting image geometry information, the latter performs both image and
semantic rendering with improved performances. Our experiments not only confirm
that GSNeRF performs favorably against prior works on both novel-view image and
semantic segmentation synthesis but the effectiveness of our sampling strategy
for visual rendering is further verified.

使用多视图输入合成新视图图像，神经辐射场（NeRF）已成为三维视觉中的热门研究课题。本文提出了一种具有一般化语义神经辐射场（GSNeRF）的方法，该方法独特地将图像语义纳入合成过程中，从而能够对未见场景生成新视图图像和相关的语义地图。我们的 GSNeRF 由两个阶段组成：语义地理推理和深度引导的视觉渲染。前者能够从场景中观察多视图图像输入，提取语义和几何特征。在后者的指导下，利用得到的图像几何信息，进行图像和语义渲染，从而提高性能。我们的实验证实了 GSNeRF 在新视图图像和语义分割合成方面优于先前的工作，并进一步验证了我们的采样策略的有效性。

GSNeRF: 增强的通用语义神经辐射场与 3D 场景理解

GSNeRF: Generalizable Semantic Neural Radiance Fields with Enhanced 3D  Scene Understanding

We introduce PhysGaussian, a new method that seamlessly integrates physically
grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel
motion synthesis. Employing a custom Material Point Method (MPM), our approach
enriches 3D Gaussian kernels with physically meaningful kinematic deformation
and mechanical stress attributes, all evolved in line with continuum mechanics
principles. A defining characteristic of our method is the seamless integration
between physical simulation and visual rendering: both components utilize the
same 3D Gaussian kernels as their discrete representations. This negates the
necessity for triangle/tetrahedron meshing, marching cubes, "cage meshes," or
any other geometry embedding, highlighting the principle of "what you see is
what you simulate (WS$^2$)." Our method demonstrates exceptional versatility
across a wide variety of materials--including elastic entities, metals,
non-Newtonian fluids, and granular materials--showcasing its strong
capabilities in creating diverse visual content with novel viewpoints and
movements. Our project page is at: this https URL

我们介绍了 PhysGaussian，一种无缝集成物理基础牛顿动力学和三维高斯函数的新方法，以实现高质量的新颖动作合成。

PhysGaussian: 三维高斯过程的物理生成动力学

PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics

The quality of a document is affected by various factors, including
grammaticality, readability, stylistics, and expertise depth, making the task
of document quality assessment a complex one. In this paper, we explore this
task in the context of assessing the quality of Wikipedia articles and academic
papers. Observing that the visual rendering of a document can capture implicit
quality indicators that are not present in the document text --- such as
images, font choices, and visual layout --- we propose a joint model that
combines the text content with a visual rendering of the document for document
quality assessment. Experimental results over two datasets reveal that textual
and visual features are complementary, achieving state-of-the-art results.

本文研究在评估维基百科文章和学术论文质量方面，采用同时结合文本内容和文档视觉渲染的联合模型来捕捉文档质量的显式和隐式特征，实验结果表明，文本和视觉特征是互补的，﻿能够达到最先进的效果。