Statistical 3D shape models of the head, hands, and fullbody are widely used
in computer vision and graphics. Despite their wide use, we show that existing
models of the head and hands fail to capture the full range of motion for these
parts. Moreover, existing work largely ignores the feet, which are crucial for
modeling human movement and have applications in biomechanics, animation, and
the footwear industry. The problem is that previous body part models are
trained using 3D scans that are isolated to the individual parts. Such data
does not capture the full range of motion for such parts, e.g. the motion of
head relative to the neck. Our observation is that full-body scans provide
important information about the motion of the body parts. Consequently, we
propose a new learning scheme that jointly trains a full-body model and
specific part models using a federated dataset of full-body and body-part
scans. Specifically, we train an expressive human body model called SUPR
(Sparse Unified Part-Based Human Representation), where each joint strictly
influences a sparse set of model vertices. The factorized representation
enables separating SUPR into an entire suite of body part models. Note that the
feet have received little attention and existing 3D body models have highly
under-actuated feet. Using novel 4D scans of feet, we train a model with an
extended kinematic tree that captures the range of motion of the toes.
Additionally, feet deform due to ground contact. To model this, we include a
novel non-linear deformation function that predicts foot deformation
conditioned on the foot pose, shape, and ground contact. We train SUPR on an
unprecedented number of scans: 1.2 million body, head, hand and foot scans. We
quantitatively compare SUPR and the separated body parts and find that our
suite of models generalizes better than existing models. SUPR is available at
this http URL

本文提出了一种名为 SUPR 的新型人体建模方案，联合训练了全身模型和特定部位模型，其中包括一种能够捕捉脚部运动特征的新型运动学树，并且利用非线性变形函数预测脚部在接触地面时的变形。

SUPR：一种稀疏的统一分部人体表示

SUPR: A Sparse Unified Part-Based Human Representation

3D shape models are naturally parameterized using vertices and faces, \ie,
composed of polygons forming a surface. However, current 3D learning paradigms
for predictive and generative tasks using convolutional neural networks focus
on a voxelized representation of the object. Lifting convolution operators from
the traditional 2D to 3D results in high computational overhead with little
additional benefit as most of the geometry information is contained on the
surface boundary. Here we study the problem of directly generating the 3D shape
surface of rigid and non-rigid shapes using deep convolutional neural networks.
We develop a procedure to create consistent `geometry images' representing the
shape surface of a category of 3D objects. We then use this consistent
representation for category-specific shape surface generation from a parametric
representation or an image by developing novel extensions of deep residual
networks for the task of geometry image generation. Our experiments indicate
that our network learns a meaningful representation of shape surfaces allowing
it to interpolate between shape orientations and poses, invent new shape
surfaces and reconstruct 3D shape surfaces from previously unseen images.

利用几何图像的形式通过卷积神经网络，直接生成三维形状表面，实现形状表面的插值、发明以及从未看见的图像中恢复三维形状表面等功能。

SurfNet：使用深度残差网络生成三维形状表面

SurfNet: Generating 3D shape surfaces using deep residual networks

3D shape models are becoming widely available and easier to capture, making
available 3D information crucial for progress in object classification. Current
state-of-the-art methods rely on CNNs to address this problem. Recently, we
witness two types of CNNs being developed: CNNs based upon volumetric
representations versus CNNs based upon multi-view representations. Empirical
results from these two types of CNNs exhibit a large gap, indicating that
existing volumetric CNN architectures and approaches are unable to fully
exploit the power of 3D representations. In this paper, we aim to improve both
volumetric CNNs and multi-view CNNs according to extensive analysis of existing
approaches. To this end, we introduce two distinct network architectures of
volumetric CNNs. In addition, we examine multi-view CNNs, where we introduce
multi-resolution filtering in 3D. Overall, we are able to outperform current
state-of-the-art methods for both volumetric CNNs and multi-view CNNs. We
provide extensive experiments designed to evaluate underlying design choices,
thus providing a better understanding of the space of methods available for
object classification on 3D data.

本文围绕三维信息在物体分类中的重要性，通过改进现有的立体卷积神经网络和多视卷积神经网络结构，引入了基于多分辨率过滤的三维多视卷积神经网络，取得了比现有技术更好的分类效果，并提供了相关实验结果和设计思路分析。