Scene representations using 3D Gaussian primitives have produced excellent
results in modeling the appearance of static and dynamic 3D scenes. Many
graphics applications, however, demand the ability to manipulate both the
appearance and the physical properties of objects. We introduce Feature
Splatting, an approach that unifies physics-based dynamic scene synthesis with
rich semantics from vision language foundation models that are grounded by
natural language. Our first contribution is a way to distill high-quality,
object-centric vision-language features into 3D Gaussians, that enables
semi-automatic scene decomposition using text queries. Our second contribution
is a way to synthesize physics-based dynamics from an otherwise static scene
using a particle-based simulator, in which material properties are assigned
automatically via text queries. We ablate key techniques used in this pipeline,
to illustrate the challenge and opportunities in using feature-carrying 3D
Gaussians as a unified format for appearance, geometry, material properties and
semantics grounded on natural language. Project website:
this https URL

使用 3D 高斯原始物体来建模静态和动态 3D 场景的外观已经取得了优秀的结果。本研究提出了一种统一物理动态场景合成和由自然语言支持的丰富语义的特征平面化方法。通过将高质量的面向对象视觉语言特征整合为 3D 高斯模型，能够利用文本查询实现半自动场景分解。同时，通过基于粒子的模拟器从静态场景中合成物理动力学，通过文本查询自动分配材料属性。本研究在这一流程中消去了关键技术，以说明将携带特征的 3D 高斯模型作为外观、几何、材料属性和基于自然语言的语义的统一格式的挑战和机遇。

特征投射与基于语言的基于物理的场景合成和编辑

Feature Splatting: Language-Driven Physics-Based Scene Synthesis and  Editing

Recognizing out-of-distribution (OOD) samples is critical for machine
learning systems deployed in the open world. The vast majority of OOD detection
methods are driven by a single modality (e.g., either vision or language),
leaving the rich information in multi-modal representations untapped. Inspired
by the recent success of vision-language pre-training, this paper enriches the
landscape of OOD detection from a single-modal to a multi-modal regime.
Particularly, we propose Maximum Concept Matching (MCM), a simple yet effective
zero-shot OOD detection method based on aligning visual features with textual
concepts. We contribute in-depth analysis and theoretical insights to
understand the effectiveness of MCM. Extensive experiments demonstrate that MCM
achieves superior performance on a wide variety of real-world tasks. MCM with
vision-language features outperforms a common baseline with pure visual
features on a hard OOD task with semantically similar classes by 13.1% (AUROC).
Code is available at this https URL.

该研究提出 Maximum Concept Matching（MCM）这一零样本的多模态算法来识别异常数据，利用视觉特征与文本概念进行对齐。研究发现 MCM 比单模态算法在效果上更为优秀，特别是结合视觉 - 语言特征时。