无需3D扫描，用人体数据构建可解释的通用人体模型
Human Mesh Modeling for Anny Body
- 基于人体测量学知识，用参数控制人体形态变化
- 覆盖从婴儿到老人的全年龄段与体型，符合全球人口统计
- 适合生成可控合成数据或高精度人体重建，开源可用
参数化人体模型是众多以人为核心任务的基础，但现有模型多依赖昂贵的3D扫描和专有的、人口分布狭窄的形状空间。我们提出Anny，一种简单、完全可微、无需扫描的人体模型，其基础为MakeHuman社区的人体测量知识。Anny定义了一个连续且可解释的形状空间，通过性别、年龄、身高、体重等表型参数控制混合形状，涵盖从婴幼儿到老年人、多种体型与比例的人体形态。模型基于世界卫生组织（WHO）的人口统计数据校准，实现单一统一模型内的真实且具有人口代表性的形体变化。我们开源了Anny人体模型及其代码（Apache 2.0许可）。由于其开放性和语义控制能力，Anny可广泛用于3D人体建模：支持毫米级精度的扫描拟合、可控合成数据生成以及人体网格恢复（HMR）。我们还推出了Anny-One，包含78万张由Anny生成的逼真图像，表明尽管模型结构简单，基于Anny训练的HMR模型性能仍可媲美使用扫描数据训练的模型。
原文摘要 · Abstract (English)
Parametric body models provide the structural basis for many human-centric tasks, yet existing models often rely on costly 3D scans and learned shape spaces that are proprietary and demographically narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowledge from the MakeHuman community. Anny defines a continuous, interpretable shape space, where phenotype parameters (e.g. gender, age, height, weight) control blendshapes spanning a wide range of human forms---across ages (from infants to elders), body types, and proportions. Calibrated using WHO population statistics, Anny provides realistic and demographically grounded human shape variation within a single unified model. We release the Anny body model and its code under the Apache 2.0 license. Thanks to its openness and semantic control, Anny serves as a versatile foundation for 3D human modeling---supporting millimeter-accurate scan fitting, controlled synthetic data generation, and Human Mesh Recovery (HMR). We further introduce Anny-One, a collection of 780k photorealistic images generated with Anny, showing that despite its simplicity, HMR models trained with Anny can match the performance of those trained with scan-based body models.
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