3D human generation from 2D images has achieved remarkable progress through the synergistic utilization of neural rendering and generative models. Existing 3D human generative models mainly generate a clothed 3D human as an undetectable 3D model in a single pass, while rarely considering the layer-wise nature of a clothed human body, which often consists of the human body and various clothes such as underwear, outerwear, trousers, shoes, etc. In this work, we propose HumanLiff, the first layer-wise 3D human generative model with a unified diffusion process. Specifically, HumanLiff firstly generates minimal-clothed humans, represented by tri-plane features, in a canonical space, and then progressively generates clothes in a layer-wise manner. In this way, the 3D human generation is thus formulated as a sequence of diffusion-based 3D conditional generation. To reconstruct more fine-grained 3D humans with tri-plane representation, we propose a tri-plane shift operation that splits each tri-plane into three sub-planes and shifts these sub-planes to enable feature grid subdivision. To further enhance the controllability of 3D generation with 3D layered conditions, HumanLiff hierarchically fuses tri-plane features and 3D layered conditions to facilitate the 3D diffusion model learning. Extensive experiments on two layer-wise 3D human datasets, SynBody (synthetic) and TightCap (real-world), validate that HumanLiff significantly outperforms state-of-the-art methods in layer-wise 3D human generation. Our code will be available at https://skhu101.github.io/HumanLiff.

翻译：从二维图像生成三维人体已通过神经渲染与生成模型的协同应用取得了显著进展。现有三维人体生成模型主要将穿着衣物的三维人体视为整体模型一次性生成，却鲜少考虑人体本身具有分层特性——通常包含人体躯干及各类服饰（如内衣、外套、裤子、鞋子等）。本文提出HumanLiff，首个采用统一扩散流程的分层三维人体生成模型。具体而言，HumanLiff首先在规范空间通过三平面特征生成最小衣物覆盖的人体，随后以分层方式逐步生成服饰。由此，三维人体生成被形式化为基于扩散的三维条件生成序列。为通过三平面表示重建更精细的三维人体，我们提出三平面移位操作，将每个三平面拆分为三个子平面并进行移位，实现特征网格细分。为进一步增强三维分层条件下的生成可控性，HumanLiff分层融合三平面特征与三维分层条件，促进三维扩散模型的学习。在两个分层三维人体数据集SynBody（合成）与TightCap（真实场景）上的大量实验表明，HumanLiff在分层三维人体生成任务上显著优于现有最先进方法。代码将开源至https://skhu101.github.io/HumanLiff。