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 http://www.iri.upc.edu/people/ecorona/smplicit/.

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