We propose RANA, a relightable and articulated neural avatar for the photorealistic synthesis of humans under arbitrary viewpoints, body poses, and lighting. We only require a short video clip of the person to create the avatar and assume no knowledge about the lighting environment. We present a novel framework to model humans while disentangling their geometry, texture, and also lighting environment from monocular RGB videos. To simplify this otherwise ill-posed task we first estimate the coarse geometry and texture of the person via SMPL+D model fitting and then learn an articulated neural representation for photorealistic image generation. RANA first generates the normal and albedo maps of the person in any given target body pose and then uses spherical harmonics lighting to generate the shaded image in the target lighting environment. We also propose to pretrain RANA using synthetic images and demonstrate that it leads to better disentanglement between geometry and texture while also improving robustness to novel body poses. Finally, we also present a new photorealistic synthetic dataset, Relighting Humans, to quantitatively evaluate the performance of the proposed approach.

我们提出了全球环境视觉变压器（GC VIT），这是一种新的结构，可增强参数和计算利用率。我们的方法利用了与本地自我注意的联合的全球自我发项模块，以有效但有效地建模长和短距离的空间相互作用，而无需昂贵的操作，例如计算注意力面罩或移动本地窗户。此外，我们通过建议在我们的体系结构中使用修改后的融合倒置残差块来解决VIT中缺乏归纳偏差的问题。我们提出的GC VIT在图像分类，对象检测和语义分割任务中实现了最新的结果。在用于分类的ImagEnet-1k数据集上，基本，小而微小的GC VIT，$ 28 $ M，$ 51 $ M和$ 90 $ M参数实现$ \ textbf {83.2 \％} $，$ \ textbf {83.9 \％} $和$ \ textbf {84.4 \％} $ top-1的精度，超过了相当大的先前艺术，例如基于CNN的Convnext和基于VIT的Swin Transformer，其优势大大。在对象检测，实例分割和使用MS Coco和ADE20K数据集的下游任务中，预训练的GC VIT主机在对象检测，实例分割和语义分割的任务中始终如一地超过事务，有时是通过大余量。可在https://github.com/nvlabs/gcvit上获得代码。

我们介绍了ADAVIT，一种可自适应地调整视觉变压器（VIT）推理成本的方法，用于不同复杂性的图像。 Adavit通过自动减少在网络中处理的视觉变压器中的令牌数量作为推理进行的令牌的数量来实现这一目标。我们为此任务进行重新格式化自适应计算时间（ACT），扩展为丢弃冗余空间令牌。视觉变换器的吸引力架构属性使我们的自适应令牌减少机制能够加速推理而不修改网络架构或推理硬件。我们展示了ADAVIT不需要额外的参数或子网来停止，因为我们基于自适应停止在原始网络参数上的学习。我们进一步引入了与现有行为方法相比稳定培训的分布先前正则化。在图像分类任务（ImageNet1K）上，我们表明我们提出的Adavit在过滤信息丰富的空间特征和削减整体计算上产生了高效率。所提出的方法将Deit-Tiny的吞吐量提高了62％并除去了38％，只有0.3％的精度下降，优于大边距。

我们提出了一种从动态摄像机记录的单像素视频中恢复的3D全局人体网格恢复方法。即使在镜头的视野之外，我们的方法也适于严重和长期闭塞，并使人体追踪人体。为实现这一目标，我们首先提出了一种深入的生成运动infiller，该infill是基于可见运动的自向填充遮挡人体的身体运动。另外，与事先工作相比，我们的方法即使用动态摄像机也将在一致的全局坐标中重建人体网格。由于人类动作和相机姿势的联合重建是受到的，我们提出了一种全球轨迹预测因素，以基于当地机身运动产生全球人类轨迹。使用预测的轨迹作为锚点，我们介绍了一种全局优化框架，它可以改进预测的轨迹，并优化相机姿势以匹配诸如2D关键点之类的视频证据。具有动态摄像机的挑战性挑战和野外数据集的实验表明，在运动缺陷和全局网格恢复方面，所提出的方法显着优于现有方法。

我们介绍了延迟感知网络加速度（LANA） - 一种在神经结构上建立的方法，用于加速神经网络的神经结构搜索技术和教师学生蒸馏。 Lana由两个阶段组成：在第一阶段，它会使用层面特征映射蒸馏来列举每层教师网络的许多替代操作。在第二阶段，它解决了使用新颖的整数线性优化（ILP）方法的有效操作的组合选择。 ILP带来独特的属性，因为它（i）在几秒钟内执行NAS，（ii）轻松满足预算约束，（iii）在图层粒度上工作，（iv）支持巨大的搜索空间$ o（10 ^ { 100}）$，超越先前的搜索方法，效率和效率。在广泛的实验中，我们表明Lana产生了由目标潜伏期预算限制的有效和准确的模型，同时比其他技术明显快。我们分析了三个流行的网络架构：高效的网络，高效网络和reses，并在压缩较大模型的较小模型的延迟级别时，实现所有型号（高达3.0 \％$）的准确性改进。 Lana通过GPU和CPU实现显着的加速（高达5美元\倍），以没有准确性下降。代码将很快分享。

Structural pruning of neural network parameters reduces computation, energy, and memory transfer costs during inference. We propose a novel method that estimates the contribution of a neuron (filter) to the final loss and iteratively removes those with smaller scores. We describe two variations of our method using the first and secondorder Taylor expansions to approximate a filter's contribution. Both methods scale consistently across any network layer without requiring per-layer sensitivity analysis and can be applied to any kind of layer, including skip connections. For modern networks trained on ImageNet, we measured experimentally a high (>93%) correlation between the contribution computed by our methods and a reliable estimate of the true importance. Pruning with the proposed methods leads to an improvement over state-ofthe-art in terms of accuracy, FLOPs, and parameter reduction. On ResNet-101, we achieve a 40% FLOPS reduction by removing 30% of the parameters, with a loss of 0.02% in the top-1 accuracy on ImageNet. Code is available at https://github.com/NVlabs/Taylor_pruning.

We propose a new formulation for pruning convolutional kernels in neural networks to enable efficient inference. We interleave greedy criteria-based pruning with finetuning by backpropagation-a computationally efficient procedure that maintains good generalization in the pruned network. We propose a new criterion based on Taylor expansion that approximates the change in the cost function induced by pruning network parameters. We focus on transfer learning, where large pretrained networks are adapted to specialized tasks. The proposed criterion demonstrates superior performance compared to other criteria, e.g. the norm of kernel weights or feature map activation, for pruning large CNNs after adaptation to fine-grained classification tasks (Birds-200 and Flowers-102) relaying only on the first order gradient information. We also show that pruning can lead to more than 10× theoretical reduction in adapted 3D-convolutional filters with a small drop in accuracy in a recurrent gesture classifier. Finally, we show results for the largescale ImageNet dataset to emphasize the flexibility of our approach.

Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow, limiting the application scenarios of utilizing NeRF on resource-constrained hardware, such as mobile devices. Many works have been conducted to reduce the latency of running NeRF models. However, most of them still require high-end GPU for acceleration or extra storage memory, which is all unavailable on mobile devices. Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. Nevertheless, to reach a similar rendering quality as NeRF, the network in NeLF is designed with intensive computation, which is not mobile-friendly. In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering. We follow the setting of NeLF to train our network. Unlike existing works, we introduce a novel network architecture that runs efficiently on mobile devices with low latency and small size, i.e., saving $15\times \sim 24\times$ storage compared with MobileNeRF. Our model achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world scenes on mobile devices, e.g., $18.04$ms (iPhone 13) for rendering one $1008\times756$ image of real 3D scenes. Additionally, we achieve similar image quality as NeRF and better quality than MobileNeRF (PSNR $26.15$ vs. $25.91$ on the real-world forward-facing dataset).

基于人工智能的模型的快速发展需要创新的监视技术，这些技术可以实时以低计算成本实时运行。在机器学习中，尤其是如果我们考虑神经网络（NN）学习算法，尤其是深入学习的体系结构，则通常以监督的方式对模型进行培训。因此，在模型的部署过程中，输入和输出之间的学习关系必须保持有效。如果这种平稳性假设成立，我们可以得出结论，NN会产生准确的预测。否则，需要对模型的重建或重建。我们建议考虑NN生成的数据（称为“嵌入”）的潜在特征表示，以确定数据流开始为非平稳的时间点。确切地说，我们通过基于数据深度和归一化等级的计算应用多元控制图来监视嵌入。使用具有不同基础数据格式的各种NN评估引入方法的性能。