在本文中，我们解决了多视图3D形状重建的问题。尽管最近与隐式形状表示相关的最新可区分渲染方法提供了突破性的表现，但它们仍然在计算上很重，并且在估计的几何形状上通常缺乏精确性。为了克服这些局限性，我们研究了一种基于体积的新型表示形式建立的新计算方法，就像在最近的可区分渲染方法中一样，但是用深度图进行了参数化，以更好地实现形状表面。与此表示相关的形状能量可以评估给定颜色图像的3D几何形状，并且不需要外观预测，但在优化时仍然受益于体积整合。在实践中，我们提出了一个隐式形状表示，SRDF基于签名距离，我们通过沿摄像头射线进行参数化。相关的形状能量考虑了深度预测一致性和光度一致性之间的一致性，这是在体积表示内的3D位置。可以考虑各种照片一致先验的基础基线，或者像学习功能一样详细的标准。该方法保留具有深度图的像素准确性，并且可行。我们对标准数据集进行的实验表明，它提供了有关具有隐式形状表示的最新方法以及传统的多视角立体方法的最新结果。

我们提出了姿势-NDF，这是基于神经距离场（NDFS）的合理人姿势的连续模型。姿势或运动先验对于产生现实的新姿势和重建噪音或部分观察的准确姿势很重要。 Pose-NDF学习了一个合理姿势的多种姿势作为神经隐式函数的零级集合，将3D中隐式表面建模的概念扩展到高维域So（3）^k，其中人姿势由A定义为一个由A定义的。单个数据点，由k四元组表示。所得的高维隐式函数可以相对于输入姿势有区别，因此可以通过在3维超球体的集合上使用梯度下降来将任意姿势投射到歧管上。与以前基于VAE的人姿势先验相反，将姿势空间转化为高斯分布，我们对实际的姿势歧管进行了建模，并保留了姿势之间的距离。我们证明，POSENDF在各种下游任务中的先验胜过现有的最新方法，从降级现实世界的人类MOCAP数据，从遮挡数据恢复到从图像中恢复到3D姿势重建。此外，我们证明它可以用来通过随机抽样和投影来产生更多的姿势，而不是基于VAE的方法。

用于运动中的人类的新型视图综合是一个具有挑战性的计算机视觉问题，使得诸如自由视视频之类的应用。现有方法通常使用具有多个输入视图，3D监控或预训练模型的复杂设置，这些模型不会概括为新标识。旨在解决这些限制，我们提出了一种新颖的视图综合框架，以从单视图传感器捕获的任何人的看法生成现实渲染，其具有稀疏的RGB-D，类似于低成本深度摄像头，而没有参与者特定的楷模。我们提出了一种架构来学习由基于球体的神经渲染获得的小说视图中的密集功能，并使用全局上下文修复模型创建完整的渲染。此外，增强剂网络利用了整体保真度，即使在原始视图中的遮挡区域中也能够产生细节的清晰渲染。我们展示了我们的方法为单个稀疏RGB-D输入产生高质量的合成和真实人体演员的新颖视图。它概括了看不见的身份，新的姿势，忠实地重建面部表情。我们的方法优于现有人体观测合成方法，并且对不同水平的输入稀疏性具有稳健性。

最近，数据驱动的单视图重建方法在建模3D穿着人类中表现出很大的进展。然而，这种方法严重影响了单视图输入所固有的深度模糊和闭塞。在本文中，我们通过考虑一小部分输入视图并调查从这些视图中适当利用信息的最佳策略来解决这个问题。我们提出了一种数据驱动的端到端方法，其从稀疏相机视图重建穿着人的人类的隐式3D表示。具体而言，我们介绍了三个关键组件：首先是使用透视相机模型的空间一致的重建，允许使用人员在输入视图中的任意放置;第二个基于关注的融合层，用于从多个观点来看聚合视觉信息;第三种机制在多视图上下文下编码本地3D模式。在实验中，我们展示了所提出的方法优于定量和定性地在标准数据上表达现有技术。为了展示空间一致的重建，我们将我们的方法应用于动态场景。此外，我们在使用多摄像头平台获取的真实数据上应用我们的方法，并证明我们的方法可以获得与多视图立体声相当的结果，从而迅速更少的视图。

In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce models that are animation ready. In contrast, ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features. Furthermore, we propose additional per-pixel supervision on the 3D reconstruction using opacity-aware differentiable rendering. Our experiments indicate that ARCH increases the fidelity of the reconstructed humans. We obtain more than 50% lower reconstruction errors for standard metrics compared to state-of-the-art methods on public datasets. We also show numerous qualitative examples of animated, high-quality reconstructed avatars unseen in the literature so far.

Here, we demonstrate how machine learning enables the prediction of comonomers reactivity ratios based on the molecular structure of monomers. We combined multi-task learning, multi-inputs, and Graph Attention Network to build a model capable of predicting reactivity ratios based on the monomers chemical structures.

We present Second Thought, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thought not only achieves superior performance in three value alignment benchmark datasets but also shows strong human-value transfer learning ability in few-shot scenarios. The generated editing steps also offer better interpretability and ease for interactive error correction. Extensive human evaluations further confirm its effectiveness.

The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This is in contrast to most existing video recognition models, which are typically designed for short-range video analysis. This work proposes a State-Space Transformer model that can efficiently capture dependencies in long movie videos for accurate movie scene detection. Our model, dubbed TranS4mer, is built using a novel S4A building block, which combines the strengths of structured state-space sequence (S4) and self-attention (A) layers. Given a sequence of frames divided into movie shots (uninterrupted periods where the camera position does not change), the S4A block first applies self-attention to capture short-range intra-shot dependencies. Afterward, the state-space operation in the S4A block is used to aggregate long-range inter-shot cues. The final TranS4mer model, which can be trained end-to-end, is obtained by stacking the S4A blocks one after the other multiple times. Our proposed TranS4mer outperforms all prior methods in three movie scene detection datasets, including MovieNet, BBC, and OVSD, while also being $2\times$ faster and requiring $3\times$ less GPU memory than standard Transformer models. We will release our code and models.

Minimising the longest travel distance for a group of mobile robots with interchangeable goals requires knowledge of the shortest length paths between all robots and goal destinations. Determining the exact length of the shortest paths in an environment with obstacles is challenging and cannot be guaranteed in a finite time. We propose an algorithm in which the accuracy of the path planning is iteratively increased. The approach provides a certificate when the uncertainties on estimates of the shortest paths become small enough to guarantee the optimality of the goal assignment. To this end, we apply results from assignment sensitivity assuming upper and lower bounds on the length of the shortest paths. We then provide polynomial-time methods to find such bounds by applying sampling-based path planning. The upper bounds are given by feasible paths, the lower bounds are obtained by expanding the sample set and leveraging knowledge of the sample dispersion. We demonstrate the application of the proposed method with a multi-robot path-planning case study.

Many real-world applications of language models (LMs), such as code autocomplete and writing assistance, involve human-LM interaction, but the main LM benchmarks are non-interactive, where a system produces output without human intervention. To evaluate human-LM interaction, we develop a framework, Human-AI Language-based Interaction Evaluation (H-LINE), that expands non-interactive evaluation along three dimensions, capturing (i) the interactive process, not only the final output; (ii) the first-person subjective experience, not just a third-party assessment; and (iii) notions of preference beyond quality. We then design five tasks ranging from goal-oriented to open-ended to capture different forms of interaction. On four state-of-the-art LMs (three variants of OpenAI's GPT-3 and AI21's J1-Jumbo), we find that non-interactive performance does not always result in better human-LM interaction and that first-person and third-party metrics can diverge, suggesting the importance of examining the nuances of human-LM interaction.