Several leading methods on public benchmarks for depth-from-stereo rely on memory-demanding 4D cost volumes and computationally intensive 3D convolutions for feature matching. We suggest a new way to process the 4D cost volume where we merge two different concepts in one deeply integrated framework to achieve a symbiotic relationship. A feature matching part is responsible for identifying matching pixels pairs along the baseline while a concurrent image volume part is inspired by depth-from-mono CNNs. However, instead of predicting depth directly from image features, it provides additional context to resolve ambiguities during pixel matching. More technically, the processing of the 4D cost volume is separated into a 2D propagation and a 3D propagation part. Starting from feature maps of the left image, the 2D propagation assists the 3D propagation part of the cost volume at different layers by adding visual features to the geometric context. By combining both parts, we can safely reduce the scale of 3D convolution layers in the matching part without sacrificing accuracy. Experiments demonstrate that our end-to-end trained CNN is ranked 2nd on KITTI2012 and ETH3D benchmarks while being significantly faster than the 1st-ranked method. Furthermore, we notice that the coupling of image and matching-volume improves fine-scale details as demonstrated by our qualitative analysis.

We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface. The 3D points have associated semantics and can move freely in 3D space. This allows for optimal coverage of the person of interest, beyond just the body shape, which in turn, additionally helps modeling accessories, hair, and loose clothing. Owing to this, we present a complete 3D transformer-based attention framework which, given a single image of a person in an unconstrained pose, generates an animatable 3D reconstruction with albedo and illumination decomposition, as a result of a single end-to-end model, trained semi-supervised, and with no additional postprocessing. We show that our S3F model surpasses the previous state-of-the-art on various tasks, including monocular 3D reconstruction, as well as albedo and shading estimation. Moreover, we show that the proposed methodology allows novel view synthesis, relighting, and re-posing the reconstruction, and can naturally be extended to handle multiple input images (e.g. different views of a person, or the same view, in different poses, in video). Finally, we demonstrate the editing capabilities of our model for 3D virtual try-on applications.

Fires have destructive power when they break out and affect their surroundings on a devastatingly large scale. The best way to minimize their damage is to detect the fire as quickly as possible before it has a chance to grow. Accordingly, this work looks into the potential of AI to detect and recognize fires and reduce detection time using object detection on an image stream. Object detection has made giant leaps in speed and accuracy over the last six years, making real-time detection feasible. To our end, we collected and labeled appropriate data from several public sources, which have been used to train and evaluate several models based on the popular YOLOv4 object detector. Our focus, driven by a collaborating industrial partner, is to implement our system in an industrial warehouse setting, which is characterized by high ceilings. A drawback of traditional smoke detectors in this setup is that the smoke has to rise to a sufficient height. The AI models brought forward in this research managed to outperform these detectors by a significant amount of time, providing precious anticipation that could help to minimize the effects of fires further.

We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained speech model, HuBERT. To this end, we introduce a number of topological and algebraic features derived from Transformer attention maps and embeddings. We show that a simple linear classifier built on top of such features outperforms a fine-tuned classification head. In particular, we achieve an improvement of about $9\%$ accuracy and $5\%$ ERR on four common datasets; on CREMA-D, the proposed feature set reaches a new state of the art performance with accuracy $80.155$. We also show that topological features are able to reveal functional roles of speech Transformer heads; e.g., we find the heads capable to distinguish between pairs of sample sources (natural/synthetic) or voices without any downstream fine-tuning. Our results demonstrate that TDA is a promising new approach for speech analysis, especially for tasks that require structural prediction.

This paper investigates models of event implications. Specifically, how well models predict entity state-changes, by targeting their understanding of physical attributes. Nominally, Large Language models (LLM) have been exposed to procedural knowledge about how objects interact, yet our benchmarking shows they fail to reason about the world. Conversely, we also demonstrate that existing approaches often misrepresent the surprising abilities of LLMs via improper task encodings and that proper model prompting can dramatically improve performance of reported baseline results across multiple tasks. In particular, our results indicate that our prompting technique is especially useful for unseen attributes (out-of-domain) or when only limited data is available.

拟人化是一种语音人物，它赋予无生命实体具有属性和行动，通常被视为需要动画。在本文中，我们探讨了人格化生成的任务。为此，我们提出了菠萝：通过获取平行的人格化数据来学习增强的产生，来拟人化无生命的实体。我们策划了一个名为PersonifCorp的拟人化语料库，并自动生成了这些拟人化的文字化。我们通过训练SEQ2SEQ模型来拟人化给定的文字输入，从而证明了该平行语料库的有用性。自动评估和人类评估都表明，通过人格科目进行微调会带来与人格化相关的素质（例如动画和兴趣）的显着提高。详细的定性分析还强调了菠萝在基准上的关键优势和瑕疵，表明具有强大的能力产生多样化和创造性的拟人化，从而增强了句子的整体吸引力。

舌头是有意义的句子，难以发音。自动产生舌头扭曲的过程具有挑战性，因为产生的话语必须立即满足两个条件：语音难度和语义含义。此外，语音难度本身很难表征，并且通过异质的现象（例如垂涎和谐音）的异质组合以自然的扭曲词来表达。在本文中，我们提出了Pancetta：音素意识到的神经完成，以自动引起舌头扭曲。我们利用音素表示来捕获语音难度的概念，并训练语言模型以在两个提出的任务设置上生成原始的舌头扭曲。为此，我们策划了一个名为Pancetta的数据集，该数据集由现有的英语舌头组成。通过自动和人类评估以及定性分析，我们表明pancetta产生了新颖，语音上的困难，流利和语义上有意义的舌头扭曲。

事件摄像机由于其有益的特性，例如高时间分辨率，高带宽，几乎没有运动模糊和低功耗，因此在机器人技术和计算机视觉中变得越来越流行。但是，这些相机在市场上仍然昂贵且稀缺，使它们无法获得大多数。使用事件模拟器最大程度地减少了对真实事件摄像机开发新算法的需求。但是，由于模拟的计算复杂性，无法实时生成现有仿真器的事件流，而是必须从现有视频序列或预渲染中预先计算，然后从虚拟3D场景中进行模拟。尽管这些离线生成的事件流可以用作学习任务的培训数据，但所有响应时间的应用程序都无法从这些模拟器中受益，因为它们仍然需要实际的事件摄像头。这项工作提出了仿真方法，将事件模拟的性能提高了两个数量级（使其实时能够），同时在质量评估中保持竞争力。

由于意识的提高，人体工程学的风险评估现在比过去更频繁地进行。基于对工作场所的专家辅助观察和手动填写评分表的常规风险评估评估仍然是主要的。数据分析通常是在关注关键时刻的重点，尽管不支持上下文信息和随时间变化。在本文中，我们介绍了ErgoExplorer，这是一种用于风险评估数据的交互式视觉分析的系统。与当前的实践相反，我们专注于跨越多个动作和多个工人的数据，同时保留所有上下文信息。数据自动从视频流中提取。基于经过仔细研究的分析任务，我们介绍了新的观点及其相应的交互。这些观点还结合了特定领域的分数表，以确保域专家轻松采用。所有视图都集成到ErgoExplorer中，该视图依赖于协调的多个视图来通过互动来促进分析。 ErgoExplorer使得首次可以在长期播放多个操作的长时间内检查各个身体部位的风险评估之间的复杂关系。新介绍的方法支持几个详细层面的分析和探索，从一般概述到如有必要的话，请直到检查视频流中的单个帧。我们说明了将其应用于几个数据集的新提出的方法的有用性。

从单个图像中恢复人头的几何形状，同时对材料和照明进行分解是一个严重不良的问题，需要事先解决。基于3D形态模型（3DMM）及其与可区分渲染器的组合的方法已显示出令人鼓舞的结果。但是，3DMM的表现力受到限制，它们通常会产生过度平滑和身份敏捷的3D形状，仅限于面部区域。最近，使用多层感知器参数化几何形状的神经场获得了高度准确的全头部重建。这些表示形式的多功能性也已被证明可有效解开几何形状，材料和照明。但是，这些方法需要几十个输入图像。在本文中，我们介绍了Sira，该方法从单个图像中，从一个图像中重建了具有高保真度几何形状和分解的灯光和表面材料的人头头像。我们的关键成分是基于神经场的两个数据驱动的统计模型，这些模型可以解决单视3D表面重建和外观分解的歧义。实验表明，Sira获得了最新的状态导致3D头重建，同时它成功地解开了全局照明以及弥漫性和镜面反照率。此外，我们的重建适合基于物理的外观编辑和头部模型重新构建。