We present GeoSynth, a model for synthesizing satellite images with global
style and image-driven layout control. The global style control is via textual
prompts or geographic location. These enable the specification of scene
semantics or regional appearance respectively, and can be used together. We
train our model on a large dataset of paired satellite imagery, with
automatically generated captions, and OpenStreetMap data. We evaluate various
combinations of control inputs, including different types of layout controls.
Results demonstrate that our model can generate diverse, high-quality images
and exhibits excellent zero-shot generalization. The code and model checkpoints
are available at this https URL

GeoSynth 是一个模型，用于合成具有全球风格和图像驱动的布局控制的卫星图像，通过文本提示或地理位置进行全球风格控制，可以使用场景语义或区域外观，通过在大量成对卫星图像、带自动生成标题和 OpenStreetMap 数据的数据集上训练该模型，在包括不同类型的布局控制在内的控制输入方面进行评估，结果表明我们的模型可以生成多样化、高质量的图像，并展示出优异的零次泛化能力。

GeoSynth: 上下文感知高分辨率卫星图像合成

GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis

Diminished reality (DR) refers to the removal of real objects from the
environment by virtually replacing them with their background. Modern DR
frameworks use inpainting to hallucinate unobserved regions. While recent deep
learning-based inpainting is promising, the DR use case is complicated by the
need to generate coherent structure and 3D geometry (i.e., depth), in
particular for advanced applications, such as 3D scene editing. In this paper,
we propose DeepDR, a first RGB-D inpainting framework fulfilling all
requirements of DR: Plausible image and geometry inpainting with coherent
structure, running at real-time frame rates, with minimal temporal artifacts.
Our structure-aware generative network allows us to explicitly condition color
and depth outputs on the scene semantics, overcoming the difficulty of
reconstructing sharp and consistent boundaries in regions with complex
backgrounds. Experimental results show that the proposed framework can
outperform related work qualitatively and quantitatively.

Diminished reality 通过使用 inpainting 技术生成并纠正场景的图像和几何结构，DeepDR 框架在实时帧速率下运行，具有最小的时间伪影，并在复杂背景下重建锐利和一致的边界。

DeepDR: 深度结构感知的 RGB-D 修复用于减弱现实

DeepDR: Deep Structure-Aware RGB-D Inpainting for Diminished Reality

In recent years, predicting driver's focus of attention has been a very
active area of research in the autonomous driving community. Unfortunately,
existing state-of-the-art techniques achieve this by relying only on human gaze
information, thereby ignoring scene semantics. We propose a novel Semantics
Augmented GazE (SAGE) detection approach that captures driving specific
contextual information, in addition to the raw gaze. Such a combined attention
mechanism serves as a powerful tool to focus on the relevant regions in an
image frame in order to make driving both safe and efficient. Using this, we
design a complete saliency prediction framework - SAGE-Net, which modifies the
initial prediction from SAGE by taking into account vital aspects such as
distance to objects (depth), ego vehicle speed, and pedestrian crossing intent.
Exhaustive experiments conducted through four popular saliency algorithms show
that on $\mathbf{49/56\text{ }(87.5\%)}$ cases - considering both the overall
dataset and crucial driving scenarios, SAGE outperforms existing techniques
without any additional computational overhead during the training process. The
augmented dataset along with the relevant code are available as part of the
supplementary material.

该论文提出了一种基于场景语义和注视信息相结合的自动驾驶注意力预测方法 SAGE，并通过修改自动驾驶的关键预测参数，设计了一个完整的预测框架 SAGE-Net，实验证明 SAGE 在不增加计算量的情况下，可以优于现有技术 87.5% 的案例。