Scene representation-the process of converting visual sensory data into concise descriptions-is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.

为了帮助代理在其构建块方面的场景的原因，我们希望提取任何给定场景的组成结构（特别是包括场景的对象的配置和特征）。当需要推断出现在代理的位置/观点的同时需要推断场景结构时，这个问题特别困难，因为两个变量共同引起代理人的观察。我们提出了一个无监督的变分方法来解决这个问题。利用不同场景存在的共享结构，我们的模型学会从RGB视频输入推断出两组潜在表示：一组“对象”潜伏，对应于场景的时间不变，对象级内容，如以及一组“帧”潜伏，对应于全局时变元素，例如视点。这种潜水所的分解允许我们的模型Simone，以单独的方式表示对象属性，其不依赖于视点。此外，它允许我们解解对象动态，并将其轨迹总结为时间抽象的，查看 - 不变，每个对象属性。我们在三个程序生成的视频数据集中展示了这些功能，以及在查看合成和实例分段方面的模型的性能。

综合照片 - 现实图像和视频是计算机图形的核心，并且是几十年的研究焦点。传统上，使用渲染算法（如光栅化或射线跟踪）生成场景的合成图像，其将几何形状和材料属性的表示为输入。统称，这些输入定义了实际场景和呈现的内容，并且被称为场景表示（其中场景由一个或多个对象组成）。示例场景表示是具有附带纹理的三角形网格（例如，由艺术家创建），点云（例如，来自深度传感器），体积网格（例如，来自CT扫描）或隐式曲面函数（例如，截短的符号距离）字段）。使用可分辨率渲染损耗的观察结果的这种场景表示的重建被称为逆图形或反向渲染。神经渲染密切相关，并将思想与经典计算机图形和机器学习中的思想相结合，以创建用于合成来自真实观察图像的图像的算法。神经渲染是朝向合成照片现实图像和视频内容的目标的跨越。近年来，我们通过数百个出版物显示了这一领域的巨大进展，这些出版物显示了将被动组件注入渲染管道的不同方式。这种最先进的神经渲染进步的报告侧重于将经典渲染原则与学习的3D场景表示结合的方法，通常现在被称为神经场景表示。这些方法的一个关键优势在于它们是通过设计的3D-一致，使诸如新颖的视点合成捕获场景的应用。除了处理静态场景的方法外，我们还涵盖了用于建模非刚性变形对象的神经场景表示...

We present a framework for efficient inference in structured image models that explicitly reason about objects. We achieve this by performing probabilistic inference using a recurrent neural network that attends to scene elements and processes them one at a time. Crucially, the model itself learns to choose the appropriate number of inference steps. We use this scheme to learn to perform inference in partially specified 2D models (variable-sized variational auto-encoders) and fully specified 3D models (probabilistic renderers). We show that such models learn to identify multiple objects -counting, locating and classifying the elements of a scenewithout any supervision, e.g., decomposing 3D images with various numbers of objects in a single forward pass of a neural network at unprecedented speed. We further show that the networks produce accurate inferences when compared to supervised counterparts, and that their structure leads to improved generalization.

Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3Dstructure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3Dstructure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous functions that map world coordinates to a feature representation of local scene properties. By formulating the image formation as a differentiable ray-marching algorithm, SRNs can be trained end-toend from only 2D images and their camera poses, without access to depth or shape. This formulation naturally generalizes across scenes, learning powerful geometry and appearance priors in the process. We demonstrate the potential of SRNs by evaluating them for novel view synthesis, few-shot reconstruction, joint shape and appearance interpolation, and unsupervised discovery of a non-rigid face model. 1

Generative models, as an important family of statistical modeling, target learning the observed data distribution via generating new instances. Along with the rise of neural networks, deep generative models, such as variational autoencoders (VAEs) and generative adversarial network (GANs), have made tremendous progress in 2D image synthesis. Recently, researchers switch their attentions from the 2D space to the 3D space considering that 3D data better aligns with our physical world and hence enjoys great potential in practice. However, unlike a 2D image, which owns an efficient representation (i.e., pixel grid) by nature, representing 3D data could face far more challenges. Concretely, we would expect an ideal 3D representation to be capable enough to model shapes and appearances in details, and to be highly efficient so as to model high-resolution data with fast speed and low memory cost. However, existing 3D representations, such as point clouds, meshes, and recent neural fields, usually fail to meet the above requirements simultaneously. In this survey, we make a thorough review of the development of 3D generation, including 3D shape generation and 3D-aware image synthesis, from the perspectives of both algorithms and more importantly representations. We hope that our discussion could help the community track the evolution of this field and further spark some innovative ideas to advance this challenging task.

机器学习的最近进步已经创造了利用一类基于坐标的神经网络来解决视觉计算问题的兴趣，该基于坐标的神经网络在空间和时间跨空间和时间的场景或对象的物理属性。我们称之为神经领域的这些方法已经看到在3D形状和图像的合成中成功应用，人体的动画，3D重建和姿势估计。然而，由于在短时间内的快速进展，许多论文存在，但尚未出现全面的审查和制定问题。在本报告中，我们通过提供上下文，数学接地和对神经领域的文学进行广泛综述来解决这一限制。本报告涉及两种维度的研究。在第一部分中，我们通过识别神经字段方法的公共组件，包括不同的表示，架构，前向映射和泛化方法来专注于神经字段的技术。在第二部分中，我们专注于神经领域的应用在视觉计算中的不同问题，超越（例如，机器人，音频）。我们的评论显示了历史上和当前化身的视觉计算中已覆盖的主题的广度，展示了神经字段方法所带来的提高的质量，灵活性和能力。最后，我们展示了一个伴随着贡献本综述的生活版本，可以由社区不断更新。

学习以上对象的场景表示对于实现复杂场景的结构理解和抽象至关重要。然而，由于目前为无监督的对象表示学习的方法建立在静止观察者假设或静态场景假设之上，它们通常是：i）遭受单视图空间歧义，或ii）从动态场景中不正确或不准确的对象表示。为了解决此问题，我们提出了动态感知的多目标网络（DYMON），这是一种扩展多视图以对象的表示学习学习到动态场景的方法的方法。我们在多视图 - 动态场景数据上训练Dymon，并显示Dymon学习 - 没有监督 - 从一系列观察序列来构建观察者动作和场景对象动态的纠缠效果，并构建适合渲染的场景对象空间表示在任意次（跨时间查询）和任意视点（查询空间）。我们还显示分解场景表示（W.R.T.对象）支持通过独立和时间通过空间和时间查询单个对象。

尽管深度强化学习（RL）最近取得了许多成功，但其方法仍然效率低下，这使得在数据方面解决了昂贵的许多问题。我们的目标是通过利用未标记的数据中的丰富监督信号来进行学习状态表示，以解决这一问题。本文介绍了三种不同的表示算法，可以访问传统RL算法使用的数据源的不同子集使用：（i）GRICA受到独立组件分析（ICA）的启发，并训练深层神经网络以输出统计独立的独立特征。输入。 Grica通过最大程度地减少每个功能与其他功能之间的相互信息来做到这一点。此外，格里卡仅需要未分类的环境状态。 （ii）潜在表示预测（LARP）还需要更多的上下文：除了要求状态作为输入外，它还需要先前的状态和连接它们的动作。该方法通过预测当前状态和行动的环境的下一个状态来学习状态表示。预测器与图形搜索算法一起使用。 （iii）重新培训通过训练深层神经网络来学习国家表示，以学习奖励功能的平滑版本。该表示形式用于预处理输入到深度RL，而奖励预测指标用于奖励成型。此方法仅需要环境中的状态奖励对学习表示表示。我们发现，每种方法都有其优势和缺点，并从我们的实验中得出结论，包括无监督的代表性学习在RL解决问题的管道中可以加快学习的速度。

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

人类对我们周围的3D环境具有强烈直观的理解。我们大脑的物理学的心理模型适用于不同材料的物体，使我们能够执行远远超过当前机器人的范围的广泛操纵任务。在这项工作中，我们希望纯粹从2D视觉观测学习动态3D场景的模型。我们的模型将神经辐射字段（NERF）和时间对比学习与自动码框架相结合，这将学习ViewPoint-Invariant的3D感知场景表示。我们表明，通过学习的表示空间构造的动态模型使得能够控制涉及刚体和流体的挑战操纵任务，其中在不同于机器人操作的视点中指定目标。当与自动解码框架耦合时，它甚至可以从训练分布外的相机视点支持目标规范。我们进一步通过执行未来的预测和新颖观看综合来展示学习3D动态模型的丰富性。最后，我们提供了关于不同系统设计和对学习象征的定性分析的详细融合研究。

学习以上对象的多对象场景表示是对机器智能的有希望的方法，促进了从视觉感觉数据的高级推理和控制。然而，对无监督以上的对象的场景表示的电流方法无法从场景的多个观察中聚合信息。结果，这些“单视图”方法仅基于单个2D观察（视图）形成其3D场景的表示。当然，这导致了几种不准确性，这些方法将受害者下降到单视空间歧义。为了解决此问题，我们提出了多视图和多目标网络（MULMON） - 一种通过利用多个视图学习准确，对象形式的对象场景的表示方法。为了索取跨视图的多对象多视图方案 - 维护对象对象的主要技术难度 - 云母迭代更新多个视图上的场景的潜在对象表示。为了确保这些迭代更新确实汇总空间信息以形成完整的3D场景理解，因此被要求在训练期间从新的观点来预测场景的外观。通过实验，我们表明云母更好地解决了空间歧义，而不是单视图 - 学习更准确和解散的对象表示 - 并且还实现了预测新颖观点的对象分段的新功能。

where the highest resolution is required, using facial performance capture as a case in point.

一个沿着城市街道行走的人试图对世界各个方面进行建模，这很快就会被许多商店，汽车和人们遵循自己的复杂且难以理解的动态所淹没。在这种环境中的探索和导航是一项日常任务，不需要大量精神资源。是否可以将这种感官信息的消防软管转变为最小的潜在状态，这是代理在世界上成功采取行动的必要和足够的？我们具体地提出了这个问题，并提出了可控制的状态发现算法（AC-State），该算法具有理论保证，并且实际上被证明可以发现\ textit {最小可控的潜在状态}，其中包含所有用于控制控制的信息代理，同时完全丢弃所有无关的信息。该算法由一个具有信息瓶颈的多步逆模型（预测遥远观察结果的动作）组成。 AC-State可以在没有奖励或示威的情况下实现本地化，探索和导航。我们证明了在三个领域中发现可控潜在状态的发现：将机器人组分散注意力（例如，照明条件和背景变化），与其他代理商一起在迷宫中进行探索，并在Matterport House Simulator中导航。

传统上，本征成像或内在图像分解被描述为将图像分解为两层：反射率，材料的反射率;和一个阴影，由光和几何之间的相互作用产生。近年来，深入学习技术已广泛应用，以提高这些分离的准确性。在本调查中，我们概述了那些在知名内在图像数据集和文献中使用的相关度量的结果，讨论了预测所需的内在图像分解的适用性。虽然Lambertian的假设仍然是许多方法的基础，但我们表明，对图像形成过程更复杂的物理原理组件的潜力越来越意识到，这是光学准确的材料模型和几何形状，更完整的逆轻型运输估计。考虑使用的前瞻和模型以及驾驶分解过程的学习架构和方法，我们将这些方法分类为分解的类型。考虑到最近神经，逆和可微分的渲染技术的进步，我们还提供了关于未来研究方向的见解。

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

尽管当前的视觉算法在许多具有挑战性的任务上都表现出色，但尚不清楚他们如何理解现实世界环境的物理动态。在这里，我们介绍了Physion，一种数据集和基准，用于严格评估预测物理场景如何随着时间而发展的能力。我们的数据集具有对各种物理现象的现实模拟，包括刚性和软体体碰撞，稳定的多对象配置，滚动，滑动和弹丸运动，因此比以前的基准提供了更全面的挑战。我们使用Physion来基准一套模型，其体系结构，学习目标，投入输出结构和培训数据各不相同。同时，我们在同一场景上获得了人类预测行为的精确测量，从而使我们能够直接评估任何模型能够近似人类行为的效果。我们发现，学习以对象为中心的表示的视觉算法通常优于那些没有人的表现，但仍未达到人类绩效。另一方面，绘制具有直接访问物理状态信息的神经网络的表现效果更好，并且做出与人类制作的预测更相似。这些结果表明，提取场景的物理表征是在视力算法中实现人类水平和类似人类的物理理解的主要瓶颈。我们已公开发布了所有数据和代码，以促进使用物理以完全可重现的方式对其他模型进行基准测试，从而使对视觉算法的进度进行系统的评估，这些算法像人们一样坚固地了解物理环境。

Two less addressed issues of deep reinforcement learning are (1) lack of generalization capability to new target goals, and (2) data inefficiency i.e., the model requires several (and often costly) episodes of trial and error to converge, which makes it impractical to be applied to real-world scenarios. In this paper, we address these two issues and apply our model to the task of target-driven visual navigation. To address the first issue, we propose an actor-critic model whose policy is a function of the goal as well as the current state, which allows to better generalize. To address the second issue, we propose AI2-THOR framework, which provides an environment with highquality 3D scenes and physics engine. Our framework enables agents to take actions and interact with objects. Hence, we can collect a huge number of training samples efficiently.We show that our proposed method (1) converges faster than the state-of-the-art deep reinforcement learning methods, (2) generalizes across targets and across scenes, (3) generalizes to a real robot scenario with a small amount of fine-tuning (although the model is trained in simulation), ( 4) is end-to-end trainable and does not need feature engineering, feature matching between frames or 3D reconstruction of the environment.The supplementary video can be accessed at the following link: https://youtu.be/SmBxMDiOrvs.

In recent years, substantial progress has been achieved in learning-based reconstruction of 3D objects. At the same time, generative models were proposed that can generate highly realistic images. However, despite this success in these closely related tasks, texture reconstruction of 3D objects has received little attention from the research community and state-of-the-art methods are either limited to comparably low resolution or constrained experimental setups. A major reason for these limitations is that common representations of texture are inefficient or hard to interface for modern deep learning techniques. In this paper, we propose Texture Fields, a novel texture representation which is based on regressing a continuous 3D function parameterized with a neural network. Our approach circumvents limiting factors like shape discretization and parameterization, as the proposed texture representation is independent of the shape representation of the 3D object. We show that Texture Fields are able to represent high frequency texture and naturally blend with modern deep learning techniques. Experimentally, we find that Texture Fields compare favorably to state-of-the-art methods for conditional texture reconstruction of 3D objects and enable learning of probabilistic generative models for texturing unseen 3D models. We believe that Texture Fields will become an important building block for the next generation of generative 3D models.

我们提出了神经演员（NA），一种用于从任意观点和任意可控姿势的高质量合成人类的新方法。我们的方法是基于最近的神经场景表示和渲染工作，从而从仅从2D图像中学习几何形状和外观的表示。虽然现有的作品令人兴奋地呈现静态场景和动态场景的播放，具有神经隐含方法的照片 - 现实重建和人类的渲染，特别是在用户控制的新颖姿势下，仍然很困难。为了解决这个问题，我们利用一个粗体模型作为将周围的3D空间的代理放入一个规范姿势。神经辐射场从多视图视频输入中了解在规范空间中的姿势依赖几何变形和姿势和视图相关的外观效果。为了综合高保真动态几何和外观的新颖视图，我们利用身体模型上定义的2D纹理地图作为预测残余变形和动态外观的潜变量。实验表明，我们的方法能够比播放的最先进，以及新的姿势合成来实现更好的质量，并且甚至可以概括到新的姿势与训练姿势不同的姿势。此外，我们的方法还支持对合成结果的体形控制。