本文提出了运动拼图，这是一个新型的运动风格转移网络，在几个重要方面都可以提高最先进的方式。运动难题是第一个可以控制各个身体部位运动样式的动作，从而可以进行本地样式编辑并大大增加风格化运动的范围。我们的框架旨在保持人的运动学结构，从多种样式运动中提取了风格的特征，用于不同的身体部位，并将其本地转移到目标身体部位。另一个主要优点是，它可以通过整合自适应实例正常化和注意力模块，同时保持骨骼拓扑结构，从而传递全球和本地运动风格的特征。因此，它可以捕获动态运动所表现出的样式，例如拍打和惊人，比以前的工作要好得多。此外，我们的框架允许使用样式标签或运动配对的数据集进行任意运动样式传输，从而使许多公开的运动数据集可用于培训。我们的框架可以轻松地与运动生成框架集成，以创建许多应用程序，例如实时运动传输。我们通过许多示例和以前的工作比较来证明我们的框架的优势。

社会VR，绩效捕获和虚拟试验的领域通常面临着忠实地在虚拟世界中重现真正的服装。一项关键的任务是由于织物特性，物理力和与身体接触而导致的固有服装形状不构成形状。我们建议使用一种逼真而紧凑的服装描述来促进固有的服装形状估计。另一个主要挑战是该域中的形状和设计多样性。 3D服装深度学习的最常见方法是为单个服装或服装类型建立专门的模型。我们认为，为各种服装设计建立统一的模型具有对新型服装类型的概括的好处，因此涵盖了比单个模型更大的设计领域。我们介绍了Neuraltailor，这是一种基于点级的新型架构，以可变的基数为基础回归，并将其应用于从3D点重建2D服装缝制模式的任务，可以使用服装模型。我们的实验表明，NeuralTailor成功地重建了缝纫模式，并将其推广到训练过程中未见模式拓扑的服装类型。

The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\text{C}^{3}$G-NeRF exhibits a Fr\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\text{C}^{3}$G-NeRF.

Cellular automata (CA) captivate researchers due to teh emergent, complex individualized behavior that simple global rules of interaction enact. Recent advances in the field have combined CA with convolutional neural networks to achieve self-regenerating images. This new branch of CA is called neural cellular automata [1]. The goal of this project is to use the idea of idea of neural cellular automata to grow prediction machines. We place many different convolutional neural networks in a grid. Each conv net cell outputs a prediction of what the next state will be, and minimizes predictive error. Cells received their neighbors' colors and fitnesses as input. Each cell's fitness score described how accurate its predictions were. Cells could also move to explore their environment and some stochasticity was applied to movement.

There is a dramatic shortage of skilled labor for modern vineyards. The Vinum project is developing a mobile robotic solution to autonomously navigate through vineyards for winter grapevine pruning. This necessitates an autonomous navigation stack for the robot pruning a vineyard. The Vinum project is using the quadruped robot HyQReal. This paper introduces an architecture for a quadruped robot to autonomously move through a vineyard by identifying and approaching grapevines for pruning. The higher level control is a state machine switching between searching for destination positions, autonomously navigating towards those locations, and stopping for the robot to complete a task. The destination points are determined by identifying grapevine trunks using instance segmentation from a Mask Region-Based Convolutional Neural Network (Mask-RCNN). These detections are sent through a filter to avoid redundancy and remove noisy detections. The combination of these features is the basis for the proposed architecture.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal, we represent a scene with discrete class labels, i.e., categorical distribution, to assign multiple objects into semantic categories. Thus, we extend discrete diffusion models to learn scene-scale categorical distributions. In addition, we validate that a latent diffusion model can reduce computation costs for training and deploying. To the best of our knowledge, our work is the first to apply discrete and latent diffusion for 3D categorical data on a scene-scale. We further propose to perform semantic scene completion (SSC) by learning a conditional distribution using our diffusion model, where the condition is a partial observation in a sparse point cloud. In experiments, we empirically show that our diffusion models not only generate reasonable scenes, but also perform the scene completion task better than a discriminative model. Our code and models are available at https://github.com/zoomin-lee/scene-scale-diffusion

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^d$, we obtain the following results (up to polylogarithmic factors). We give a parallel algorithm obtaining optimization error $\epsilon_{\text{opt}}$ with $d^{1/3}\epsilon_{\text{opt}}^{-2/3}$ gradient oracle query depth and $d^{1/3}\epsilon_{\text{opt}}^{-2/3} + \epsilon_{\text{opt}}^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text{opt}} \in [d^{-1}, d^{-1/4}]$, our algorithm matches the state-of-the-art oracle depth of [BJLLS19] while maintaining the optimal total work of stochastic gradient descent. We give an $(\epsilon_{\text{dp}}, \delta)$-differentially private algorithm which, given $n$ samples of Lipschitz loss functions, obtains near-optimal optimization error and makes $\min(n, n^2\epsilon_{\text{dp}}^2 d^{-1}) + \min(n^{4/3}\epsilon_{\text{dp}}^{1/3}, (nd)^{2/3}\epsilon_{\text{dp}}^{-1})$ queries to the gradients of these functions. In the regime $d \le n \epsilon_{\text{dp}}^{2}$, where privacy comes at no cost in terms of the optimal loss up to constants, our algorithm uses $n + (nd)^{2/3}\epsilon_{\text{dp}}^{-1}$ queries and improves recent advancements of [KLL21, AFKT21]. In the moderately low-dimensional setting $d \le \sqrt n \epsilon_{\text{dp}}^{3/2}$, our query complexity is near-linear.

We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting. We introduce an adaptive transfer algorithm that learns the similarities among the data sources by utilizing Random Fourier Features to disentangle the loss function into multiple components, each of which is associated with a data source. The data sources may have different distributions; the causal effects are independently and systematically incorporated. The proposed method estimates the similarities among the sources through transfer coefficients, and hence requiring no prior information about the similarity measures. The heterogeneous causal effects can be estimated with no sharing of the raw training data among the sources, thus minimizing the risk of privacy leak. We also provide minimax lower bounds to assess the quality of the parameters learned from the disparate sources. The proposed method is empirically shown to outperform the baselines on decentralized data sources with dissimilar distributions.

Given a large graph with few node labels, how can we (a) identify the mixed network-effect of the graph and (b) predict the unknown labels accurately and efficiently? This work proposes Network Effect Analysis (NEA) and UltraProp, which are based on two insights: (a) the network-effect (NE) insight: a graph can exhibit not only one of homophily and heterophily, but also both or none in a label-wise manner, and (b) the neighbor-differentiation (ND) insight: neighbors have different degrees of influence on the target node based on the strength of connections. NEA provides a statistical test to check whether a graph exhibits network-effect or not, and surprisingly discovers the absence of NE in many real-world graphs known to have heterophily. UltraProp solves the node classification problem with notable advantages: (a) Accurate, thanks to the network-effect (NE) and neighbor-differentiation (ND) insights; (b) Explainable, precisely estimating the compatibility matrix; (c) Scalable, being linear with the input size and handling graphs with millions of nodes; and (d) Principled, with closed-form formula and theoretical guarantee. Applied on eight real-world graph datasets, UltraProp outperforms top competitors in terms of accuracy and run time, requiring only stock CPU servers. On a large real-world graph with 1.6M nodes and 22.3M edges, UltraProp achieves more than 9 times speedup (12 minutes vs. 2 hours) compared to most competitors.