In this paper we introduce SMPLicit, a novel generative model to jointly
represent body pose, shape and clothing geometry. In contrast to existing
learning-based approaches that require training specific models for each type
of garment, SMPLicit can represent in a unified manner different garment
topologies (e.g. from sleeveless tops to hoodies and to open jackets), while
controlling other properties like the garment size or tightness/looseness. We
show our model to be applicable to a large variety of garments including
T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The
representation flexibility of SMPLicit builds upon an implicit model
conditioned with the SMPL human body parameters and a learnable latent space
which is semantically interpretable and aligned with the clothing attributes.
The proposed model is fully differentiable, allowing for its use into larger
end-to-end trainable systems. In the experimental section, we demonstrate
SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in
images of dressed people. In both cases we are able to go beyond state of the
art, by retrieving complex garment geometries, handling situations with
multiple clothing layers and providing a tool for easy outfit editing. To
stimulate further research in this direction, we will make our code and model
publicly available at this http URL

该论文介绍了一种新型的生成模型 SMPLicit，可以联合表示人体姿势、形状和衣物几何形状，并将不同类型的衣物拓扑结构（如无袖上衣、连帽衫和开衫）以一种统一的方式表示，同时控制其他属性如大小或紧身 / 松身。SMPLicit 建立在对 SMPL 人体参数的条件隐式模型和可学习潜在空间上，具有很高的表达灵活性，可以被用于 3D 扫描、3D 重建和服装编辑。

SMPLicit：面向穿着人群的拓扑感知生成模型

SMPLicit: Topology-aware Generative Model for Clothed People

Recent advances in 3D human shape estimation build upon parametric
representations that model very well the shape of the naked body, but are not
appropriate to represent the clothing geometry. In this paper, we present an
approach to model dressed humans and predict their geometry from single images.
We contribute in three fundamental aspects of the problem, namely, a new
dataset, a novel shape parameterization algorithm and an end-to-end deep
generative network for predicting shape.
First, we present 3DPeople, a large-scale synthetic dataset with 2.5 Million
photo-realistic images of 80 subjects performing 70 activities and wearing
diverse outfits. Besides providing textured 3D meshes for clothes and body, we
annotate the dataset with segmentation masks, skeletons, depth, normal maps and
optical flow. All this together makes 3DPeople suitable for a plethora of
tasks.
We then represent the 3D shapes using 2D geometry images. To build these
images we propose a novel spherical area-preserving parameterization algorithm
based on the optimal mass transportation method. We show this approach to
improve existing spherical maps which tend to shrink the elongated parts of the
full body models such as the arms and legs, making the geometry images
incomplete.
Finally, we design a multi-resolution deep generative network that, given an
input image of a dressed human, predicts his/her geometry image (and thus the
clothed body shape) in an end-to-end manner. We obtain very promising results
in jointly capturing body pose and clothing shape, both for synthetic
validation and on the wild images.

本文提出了一种从单张照片中预测着装人体几何形状的新方法，包括三个关键点：一个新数据集，一个新的形状参数化算法和一个端到端的深度生成网络。