自监督模型可高效生成人体扫描与动作的低维表示，支持多种动作操作。
Self Supervised Networks for Learning Latent Space Representations of Human Body Scans and Motions
- 用变流形形状编码器从无注册网格快速提取人体形态和姿态嵌入
- 在SMPL潜空间中通过线性插值实现动作序列建模，支持平滑运动生成
- 适合做动作合成、迁移与随机生成，计算开销极低
本文提出自监督神经网络模型，解决3D人体分析中的若干基础问题。首先，提出VariShaPE（变流形形状参数估计器），一种新型架构，用于从任意未对齐网格中快速鲁棒地提取人体形态与姿态的潜空间表示。其次，引入MoGeN（运动几何网络），该框架在潜空间内学习几何结构：将人体姿态参数空间映射到高维欧氏空间，使训练数据集中4D人体动作短序列可通过简单线性插值近似。基于SMPL潜空间表示，实验表明，训练完成后，该组合模型可以极低计算成本完成动作插值、外推、迁移及随机形态与姿态生成等任务。
原文摘要 · Abstract (English)
This paper introduces self-supervised neural network models to tackle several fundamental problems in the field of 3D human body analysis and processing. First, we propose VariShaPE (Varifold Shape Parameter Estimator), a novel architecture for the retrieval of latent space representations of body shapes and poses. This network offers a fast and robust method to estimate the embedding of arbitrary unregistered meshes into the latent space. Second, we complement the estimation of latent codes with MoGeN (Motion Geometry Network) a framework that learns the geometry on the latent space itself. This is achieved by lifting the body pose parameter space into a higher dimensional Euclidean space in which body motion mini-sequences from a training set of 4D data can be approximated by simple linear interpolation. Using the SMPL latent space representation we illustrate how the combination of these network models, once trained, can be used to perform a variety of tasks with very limited computational cost. This includes operations such as motion interpolation, extrapolation and transfer as well as random shape and pose generation.
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