StructLDM: Structured Latent Diffusion for 3D Human Generation

• URL: http://arxiv.org/abs/2404.01241v3
• Date: Tue, 2 Jul 2024 15:53:03 GMT
• Title: StructLDM: Structured Latent Diffusion for 3D Human Generation
• Authors: Tao Hu, Fangzhou Hong, Ziwei Liu,
• Abstract summary: StructLDM is a diffusion-based unconditional 3D human generative model learned from 2D images. It enables different levels of controllable 3D human generation and editing, including pose/view/shape control, and high-level tasks including compositional generations, part-aware clothing editing, 3D virtual try-on, etc.
• Score: 45.51684124904457
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent 3D human generative models have achieved remarkable progress by learning 3D-aware GANs from 2D images. However, existing 3D human generative methods model humans in a compact 1D latent space, ignoring the articulated structure and semantics of human body topology. In this paper, we explore more expressive and higher-dimensional latent space for 3D human modeling and propose StructLDM, a diffusion-based unconditional 3D human generative model, which is learned from 2D images. StructLDM solves the challenges imposed due to the high-dimensional growth of latent space with three key designs: 1) A semantic structured latent space defined on the dense surface manifold of a statistical human body template. 2) A structured 3D-aware auto-decoder that factorizes the global latent space into several semantic body parts parameterized by a set of conditional structured local NeRFs anchored to the body template, which embeds the properties learned from the 2D training data and can be decoded to render view-consistent humans under different poses and clothing styles. 3) A structured latent diffusion model for generative human appearance sampling. Extensive experiments validate StructLDM's state-of-the-art generation performance and illustrate the expressiveness of the structured latent space over the well-adopted 1D latent space. Notably, StructLDM enables different levels of controllable 3D human generation and editing, including pose/view/shape control, and high-level tasks including compositional generations, part-aware clothing editing, 3D virtual try-on, etc. Our project page is at: https://taohuumd.github.io/projects/StructLDM/.

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