JADE通过关节感知的潜空间实现3D人体生成的精细控制。
JADE: Joint-aware Latent Diffusion for 3D Human Generative Modeling
- 将人体分解为关节点位置与关节特征，实现几何与语义解耦
- 采用级联扩散模型分别建模骨架结构与局部表面几何
- 在公开数据集上优于现有方法，支持高精度重建与可控编辑
3D人体生成建模在计算机视觉中备受关注，核心在于设计一个既具表现力又语义可解释的紧凑潜空间，但现有方法难以兼顾二者。本文提出JADE，一种联合感知的生成框架，能以细粒度控制人体形状变化。关键思想是构建关节感知的潜空间，将人体分解为由关节点位置建模的骨骼结构，以及由附着于每个关节的特征表征的局部表面几何。该解耦设计实现了几何与语义的双重可解释性，赋予用户灵活的控制能力。为生成在该分解下连贯且合理的3D人体，我们提出级联生成管道，使用两个扩散模型分别建模骨架结构和局部表面几何的分布。在多个公开数据集上的大量实验表明，与现有方法相比，JADE在自编码重建精度、编辑可控性和生成质量等多个任务上均表现出显著优势。
原文摘要 · Abstract (English)
Generative modeling of 3D human bodies have been studied extensively in computer vision. The core is to design a compact latent representation that is both expressive and semantically interpretable, yet existing approaches struggle to achieve both requirements. In this work, we introduce JADE, a generative framework that learns the variations of human shapes with fined-grained control. Our key insight is a joint-aware latent representation that decomposes human bodies into skeleton structures, modeled by joint positions, and local surface geometries, characterized by features attached to each joint. This disentangled latent space design enables geometric and semantic interpretation, facilitating users with flexible controllability. To generate coherent and plausible human shapes under our proposed decomposition, we also present a cascaded pipeline where two diffusions are employed to model the distribution of skeleton structures and local surface geometries respectively. Extensive experiments are conducted on public datasets, where we demonstrate the effectiveness of JADE framework in multiple tasks in terms of autoencoding reconstruction accuracy, editing controllability and generation quality compared with existing methods.
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