我们提出了一种从一个或几种视图中重建人头的纹理3D网眼的方法。由于如此少的重建​​缺乏约束，因此需要先验知识，这很难强加于传统的3D重建算法。在这项工作中，我们依靠最近引入的3D表示$ \ unicode {x2013} $ neural隐式函数$ \ unicode {x2013} $，它基于神经网络，允许自然地从数据中学习有关人类头的先验，并且直接转换为纹理网格。也就是说，我们扩展了Neus（一种最新的神经隐式函数公式），以同时代表类的多个对象（在我们的情况下）。潜在的神经网架构旨在学习这些物体之间的共同点，并概括地看不见。我们的模型仅在一百个智能手机视频上进行培训，不需要任何扫描的3D数据。之后，该模型可以以良好的效果以几种镜头或一次性模式适合新颖的头。

在这项工作中，我们将神经头部的头像技术推向百万像素分辨率，同时着重于跨驾驶合成的特别挑战性的任务，即，当驾驶图像的外观与动画源图像大不相同时。我们提出了一组新的神经体系结构和训练方法，这些方法可以利用中分辨率的视频数据和高分辨率图像数据，以达到所需的渲染图像质量和对新视图和运动的概括。我们证明，建议的架构和方法产生令人信服的高分辨率神经化身，在跨驾驶场景中表现优于竞争对手。最后，我们展示了如何将受过训练的高分辨率神经化身模型蒸馏成一个轻量级的学生模型，该模型是实时运行的，并将神经化身的身份锁定到数十个预定的源图像。实时操作和身份锁对于许多实际应用头像系统至关重要。

我们介绍了一个现实的单发网眼的人体头像创作的系统，即简称罗马。使用一张照片，我们的模型估计了特定于人的头部网格和相关的神经纹理，该神经纹理编码局部光度和几何细节。最终的化身是操纵的，可以使用神经网络进行渲染，该神经网络与野外视频数据集上的网格和纹理估计器一起训练。在实验中，我们观察到我们的系统在头部几何恢复和渲染质量方面都具有竞争性的性能，尤其是对于跨人的重新制定。请参阅结果https://samsunglabs.github.io/rome/

代表具有多个半透明彩色图层的场景是实时新型视图合成的流行和成功的选择。现有方法在平面或球形的规则间隔层上推断颜色和透明度值。在这项工作中，我们介绍了一种基于多个半透明层的新视图综合方法，具有场景适应的几何形状。我们的方法在两个阶段中介绍了立体对的这些表示。第一阶段从给定的一对视图中缩小了少数数据自适应层的几何形状。第二阶段为这些层的颜色和透明度值产生了新颖的视图合成的最终表示。重要的是，两个阶段都通过可差异化的渲染器连接，并以端到端的方式训练。在实验中，我们展示了所提出的方法在使用定期间隔的层上的优势，没有适应场景几何形状。尽管在渲染过程中较快的数量次数，但我们的方法也优于基于隐式几何表示的最近提出的IBRNET系统。查看https://samsunglabs.github.io/stereolayers的结果。

现代形象染色系统，尽管取得了重大进展，往往与大型缺失区域，复杂的几何结构和高分辨率图像斗争。我们发现这是一个主要原因之一是缺乏染色网络和损失功能的有效的接受领域。为了减轻这个问题，我们提出了一种称为大面膜修正（LAMA）的新方法。喇嘛基于i）一种新的侵略网络架构，它使用具有图像宽接收领域的快速傅里叶卷曲（FFC）; ii）高接受领域感性损失; iii）大型训练面具，可解锁前两个组件的潜力。我们的批准网络在一系列数据集中改善了最先进的，即使在具有挑战性的情况下也能实现出色的性能，例如，完成定期结构。我们的模型令人惊讶地展现得比在火车时间高于所看到的决议，并在比竞争性基线更低的参数和时间成本实现这一目标。代码可用于\ url {https:/github.com/saic-mdal/lama}。

Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, superresolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs.

We present a new deep learning architecture (called Kdnetwork) that is designed for 3D model recognition tasks and works with unstructured point clouds. The new architecture performs multiplicative transformations and shares parameters of these transformations according to the subdivisions of the point clouds imposed onto them by kdtrees. Unlike the currently dominant convolutional architectures that usually require rasterization on uniform twodimensional or three-dimensional grids, Kd-networks do not rely on such grids in any way and therefore avoid poor scaling behavior. In a series of experiments with popular shape recognition benchmarks, Kd-networks demonstrate competitive performance in a number of shape recognition tasks such as shape classification, shape retrieval and shape part segmentation.

The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems. While their image generation technique uses a slow optimization process, recently several authors have proposed to learn generator neural networks that can produce similar outputs in one quick forward pass. While generator networks are promising, they are still inferior in visual quality and diversity compared to generation-by-optimization. In this work, we advance them in two significant ways. First, we introduce an instance normalization module to replace batch normalization with significant improvements to the quality of image stylization. Second, we improve diversity by introducing a new learning formulation that encourages generators to sample unbiasedly from the Julesz texture ensemble, which is the equivalence class of all images characterized by certain filter responses. Together, these two improvements take feed forward texture synthesis and image stylization much closer to the quality of generation-via-optimization, while retaining the speed advantage.

Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled targetdomain data is necessary).As the training progresses, the approach promotes the emergence of "deep" features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation.Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-ofthe-art on Office datasets.

Selecting the number of topics in LDA models is considered to be a difficult task, for which alternative approaches have been proposed. The performance of the recently developed singular Bayesian information criterion (sBIC) is evaluated and compared to the performance of alternative model selection criteria. The sBIC is a generalization of the standard BIC that can be implemented to singular statistical models. The comparison is based on Monte Carlo simulations and carried out for several alternative settings, varying with respect to the number of topics, the number of documents and the size of documents in the corpora. Performance is measured using different criteria which take into account the correct number of topics, but also whether the relevant topics from the DGPs are identified. Practical recommendations for LDA model selection in applications are derived.