Existing data-driven methods for draping garments over human bodies, despite
being effective, cannot handle garments of arbitrary topology and are typically
not end-to-end differentiable. To address these limitations, we propose an
end-to-end differentiable pipeline that represents garments using implicit
surfaces and learns a skinning field conditioned on shape and pose parameters
of an articulated body model. To limit body-garment interpenetrations and
artifacts, we propose an interpenetration-aware pre-processing strategy of
training data and a novel training loss that penalizes self-intersections while
draping garments. We demonstrate that our method yields more accurate results
for garment reconstruction and deformation with respect to state of the art
methods. Furthermore, we show that our method, thanks to its end-to-end
differentiability, allows to recover body and garments parameters jointly from
image observations, something that previous work could not do.

我们提出了一种使用隐式曲面表示衣服、学习受关节体模型形状和姿势参数影响的蒙皮场的端到端可微管道来处理任意拓扑衣物的方法，并提出了一种防止体 - 衣物相互穿插和减少艺术品的预处理策略和新的训练损失。经实验证明，我们的方法相比于现有的方法，可以更准确地重建和变形服装，并且可以通过图像观测共同恢复身体和服装参数。

DIG: 在人体上覆盖隐式服装

DIG: Draping Implicit Garment over the Human Body

Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in
producing rational and factual responses. However, to establish long-term
relationships with users, the KDG model needs the capability to generate
responses in a desired style or attribute. Thus, we study a new problem:
Stylized Knowledge-Grounded Dialogue Generation (SKDG). It presents two
challenges: (1) How to train a SKDG model where no <context, knowledge,
stylized response> triples are available. (2) How to cohere with context and
preserve the knowledge when generating a stylized response. In this paper, we
propose a novel disentangled template rewriting (DTR) method which generates
responses via combing disentangled style templates (from monolingual stylized
corpus) and content templates (from KDG corpus). The entire framework is
end-to-end differentiable and learned without supervision. Extensive
experiments on two benchmarks indicate that DTR achieves a significant
improvement on all evaluation metrics compared with previous state-of-the-art
stylized dialogue generation methods. Besides, DTR achieves comparable
performance with the state-of-the-art KDG methods in standard KDG evaluation
setting.

本研究提出了一种基于 knowledge-grounded 的对话生成模型，称为 Disentangled Template Rewriting（DTR）模型，它能够生成符合预期风格的对话，且整个框架是可学习的，并在两个基准测试中得到了显著改进的结果。

基于知识的对话生成：解耦模板重写

Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting

Given an image with multiple people, our goal is to directly regress the pose
and shape of all the people as well as their relative depth. Inferring the
depth of a person in an image, however, is fundamentally ambiguous without
knowing their height. This is particularly problematic when the scene contains
people of very different sizes, e.g. from infants to adults. To solve this, we
need several things. First, we develop a novel method to infer the poses and
depth of multiple people in a single image. While previous work that estimates
multiple people does so by reasoning in the image plane, our method, called
BEV, adds an additional imaginary Bird's-Eye-View representation to explicitly
reason about depth. BEV reasons simultaneously about body centers in the image
and in depth and, by combing these, estimates 3D body position. Unlike prior
work, BEV is a single-shot method that is end-to-end differentiable. Second,
height varies with age, making it impossible to resolve depth without also
estimating the age of people in the image. To do so, we exploit a 3D body model
space that lets BEV infer shapes from infants to adults. Third, to train BEV,
we need a new dataset. Specifically, we create a "Relative Human" (RH) dataset
that includes age labels and relative depth relationships between the people in
the images. Extensive experiments on RH and AGORA demonstrate the effectiveness
of the model and training scheme. BEV outperforms existing methods on depth
reasoning, child shape estimation, and robustness to occlusion. The code and
dataset are released for research purposes.

该论文提出了一种基于 Bird's-Eye-View 方法的姿态回归和深度估计模型，可以同时估计单个图片中多个人的 3D 位置，解决了多尺度和年龄差异等问题，模型拥有单次计算和端到端可微特性，并在实验中展示了优秀的性能。

将人置于其位置：单目深度回归中的三维人体

Putting People in their Place: Monocular Regression of 3D People in  Depth

Regression via classification (RvC) is a common method used for regression
problems in deep learning, where the target variable belongs to a set of
continuous values. By discretizing the target into a set of non-overlapping
classes, it has been shown that training a classifier can improve neural
network accuracy compared to using a standard regression approach. However, it
is not clear how the set of discrete classes should be chosen and how it
affects the overall solution. In this work, we propose that using several
discrete data representations simultaneously can improve neural network
learning compared to a single representation. Our approach is end-to-end
differentiable and can be added as a simple extension to conventional learning
methods, such as deep neural networks. We test our method on three challenging
tasks and show that our method reduces the prediction error compared to a
baseline RvC approach while maintaining a similar model complexity.

本文提出在深度神经网络中，同时使用多个离散数据表示可以提高神经网络学习效果，并证明其比传统方法（如基于单一表示的 RvC 模型）更加有效。