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。