本文提出了一个逐步连接的光场网络（Prolif），以构成复杂的前向场景的新观点。扩散编码一个4D光场，该场允许在一个训练步骤中渲染大量射线，以实现图像或贴片级损失。直接从图像中学习神经光场很难呈现多视图一致的图像，因为它对基础3D几何形状的不了解。为了解决这个问题，我们提出了一种渐进培训计划和正则化损失，以推断训练过程中的基础几何形状，这两者都会实现多视图一致性，从而极大地提高了渲染质量。实验表明，与香草神经光场相比，我们的方法能够实现明显更好的渲染质量，并且与挑战性的LLFF数据集和闪亮对象数据集的类似NERF的渲染方法相当。此外，我们证明了与LPIP的损失更好的兼容性，以实现与不同的光条件和剪辑损失的稳健性，以控制场景的渲染方式。项目页面：https：//totoro97.github.io/projects/prolif。

直接语音到语音翻译（S2ST）模型与传统级联系统可用的数据量相比，几乎没有平行的S2ST数据遇到数据稀缺问题，该数据包括自动语音识别（ASR），机器翻译（MT）和文本到语音（TTS）合成。在这项工作中，我们使用未标记的语音数据和数据扩展来探索自我监督的预训练，以解决此问题。我们利用了最近提出的语音到单位翻译（S2UT）框架，该框架将目标语音编码为离散表示形式，并转移前训练前和有效的部分填充技术，可很好地适用于语音到文本翻译（S2T）通过研究语音编码器和离散单位解码器预训练，S2UT域。我们在西班牙语 - 英语翻译上进行的实验表明，与多任务学习相比，自我监督的预训练始终如一地提高模型性能，平均为6.6-12.1 BLEU增长，并且可以与数据增强技术相结合，以应用MT来创建弱监督监督的培训数据。音频样本可在以下网址获得：https：//facebookresearch.github.io/speech_translation/enhanced_direct_s2st_units/index.html。

我们介绍了一种无线文字语音转换（S2ST）系统，可以将来自一种语言的语音转换为另一种语言，并且可以在不需要任何文本数据的情况下构建。与文献中的现有工作不同，我们解决了模拟多扬声器目标语音的挑战，并用现实世界的S2ST数据训练系统。我们方法的关键是一种自我监督的单位语音标准化技术，该标准化技术将预先训练的语音编码器具有来自多个扬声器的配对声音，以及单个参考扬声器，以减少由于复印件引起的变化，同时保留词汇内容。只有10分钟的语音标准化的配对数据，我们在培训\ vp〜s2st数据集上的S2ST模型时获得平均3.2 BLEU增益，而不是在未标准化的语音目标上培训的基线。我们还将自动开采的S2ST数据纳入并显示额外的2.0 BLEU增益。据我们所知，我们是第一个建立无线的S2ST技术，可以用真实世界的数据培训，并为多种语言配对工作。

由于其成功在从稀疏的输入图像集合中合成了场景的新颖视图，最近越来越受欢迎。到目前为止，通过通用密度函数建模了神经体积渲染技术的几何形状。此外，使用通向嘈杂的任意水平函数的任意水平集合来提取几何形状本身，通常是低保真重建。本文的目标是改善神经体积渲染中的几何形象和重建。我们通过将体积密度建模为几何形状来实现这一点。这与以前的工作与体积密度的函数建模几何。更详细地，我们将音量密度函数定义为Laplace的累积分发功能（CDF）应用于符号距离功能（SDF）表示。这种简单的密度表示有三个好处：（i）它为神经体积渲染过程中学到的几何形状提供了有用的电感偏差; （ii）它促进了缺陷近似误差的束缚，导致观看光线的准确采样。精确的采样对于提供几何和光线的精确耦合非常重要; （iii）允许高效无监督的脱位形状和外观在体积渲染中。将此新密度表示应用于具有挑战性的场景多视图数据集生产了高质量的几何重建，表现优于相关的基线。此外，由于两者的解剖学，场景之间的切换形状和外观是可能的。

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。

Photo-realistic free-viewpoint rendering of real-world scenes using classical computer graphics techniques is challenging, because it requires the difficult step of capturing detailed appearance and geometry models. Recent studies have demonstrated promising results by learning scene representations that implicitly encode both geometry and appearance without 3D supervision. However, existing approaches in practice often show blurry renderings caused by the limited network capacity or the difficulty in finding accurate intersections of camera rays with the scene geometry. Synthesizing high-resolution imagery from these representations often requires time-consuming optical ray marching. In this work, we introduce Neural Sparse Voxel Fields (NSVF), a new neural scene representation for fast and high-quality free-viewpoint rendering. NSVF defines a set of voxel-bounded implicit fields organized in a sparse voxel octree to model local properties in each cell. We progressively learn the underlying voxel structures with a diffentiable ray-marching operation from only a set of posed RGB images. With the sparse voxel octree structure, rendering novel views can be accelerated by skipping the voxels containing no relevant scene content. Our method is typically over 10 times faster than the state-of-the-art (namely, NeRF (Mildenhall et al., 2020)) at inference time while achieving higher quality results. Furthermore, by utilizing an explicit sparse voxel representation, our method can easily be applied to scene editing and scene composition. We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering. Code and data are available at our website: https://github.com/facebookresearch/NSVF.

Arguably one of the top success stories of deep learning is transfer learning. The finding that pre-training a network on a rich source set (e.g., ImageNet) can help boost performance once fine-tuned on a usually much smaller target set, has been instrumental to many applications in language and vision. Yet, very little is known about its usefulness in 3D point cloud understanding. We see this as an opportunity considering the effort required for annotating data in 3D. In this work, we aim at facilitating research on 3D representation learning. Different from previous works, we focus on high-level scene understanding tasks. To this end, we select a suite of diverse datasets and tasks to measure the effect of unsupervised pre-training on a large source set of 3D scenes. Our findings are extremely encouraging: using a unified triplet of architecture, source dataset, and contrastive loss for pre-training, we achieve improvement over recent best results in segmentation and detection across 6 different benchmarks for indoor and outdoor, real and synthetic datasets -demonstrating that the learned representation can generalize across domains. Furthermore, the improvement was similar to supervised pre-training, suggesting that future efforts should favor scaling data collection over more detailed annotation. We hope these findings will encourage more research on unsupervised pretext task design for 3D deep learning. Our code is publicly available at https://github.com/facebookresearch/PointContrast

This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART -a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective . mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task-specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show it also enables new types of transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training.

Large training data and expensive model tweaking are standard features of deep learning for images. As a result, data owners often utilize cloud resources to develop large-scale complex models, which raises privacy concerns. Existing solutions are either too expensive to be practical or do not sufficiently protect the confidentiality of data and models. In this paper, we study and compare novel \emph{image disguising} mechanisms, DisguisedNets and InstaHide, aiming to achieve a better trade-off among the level of protection for outsourced DNN model training, the expenses, and the utility of data. DisguisedNets are novel combinations of image blocktization, block-level random permutation, and two block-level secure transformations: random multidimensional projection (RMT) and AES pixel-level encryption (AES). InstaHide is an image mixup and random pixel flipping technique \cite{huang20}. We have analyzed and evaluated them under a multi-level threat model. RMT provides a better security guarantee than InstaHide, under the Level-1 adversarial knowledge with well-preserved model quality. In contrast, AES provides a security guarantee under the Level-2 adversarial knowledge, but it may affect model quality more. The unique features of image disguising also help us to protect models from model-targeted attacks. We have done an extensive experimental evaluation to understand how these methods work in different settings for different datasets.

A storyboard is a roadmap for video creation which consists of shot-by-shot images to visualize key plots in a text synopsis. Creating video storyboards however remains challenging which not only requires association between high-level texts and images, but also demands for long-term reasoning to make transitions smooth across shots. In this paper, we propose a new task called Text synopsis to Video Storyboard (TeViS) which aims to retrieve an ordered sequence of images to visualize the text synopsis. We construct a MovieNet-TeViS benchmark based on the public MovieNet dataset. It contains 10K text synopses each paired with keyframes that are manually selected from corresponding movies by considering both relevance and cinematic coherence. We also present an encoder-decoder baseline for the task. The model uses a pretrained vision-and-language model to improve high-level text-image matching. To improve coherence in long-term shots, we further propose to pre-train the decoder on large-scale movie frames without text. Experimental results demonstrate that our proposed model significantly outperforms other models to create text-relevant and coherent storyboards. Nevertheless, there is still a large gap compared to human performance suggesting room for promising future work.