SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets

• URL: http://arxiv.org/abs/2504.06982v1
• Date: Wed, 09 Apr 2025 15:38:18 GMT
• Title: SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assets
• Authors: Yuhang Yang, Fengqi Liu, Yixing Lu, Qin Zhao, Pingyu Wu, Wei Zhai, Ran Yi, Yang Cao, Lizhuang Ma, Zheng-Jun Zha, Junting Dong,
• Abstract summary: We propose a latent space generation paradigm for 3D human digitization.<n>We transform the ill-posed low-to-high-dimensional mapping problem into a learnable distribution shift.<n>We employ the multi-view optimization approach combined with synthetic data to construct the HGS-1M dataset.
• Score: 72.26350984924129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D human digitization has long been a highly pursued yet challenging task. Existing methods aim to generate high-quality 3D digital humans from single or multiple views, but remain primarily constrained by current paradigms and the scarcity of 3D human assets. Specifically, recent approaches fall into several paradigms: optimization-based and feed-forward (both single-view regression and multi-view generation with reconstruction). However, they are limited by slow speed, low quality, cascade reasoning, and ambiguity in mapping low-dimensional planes to high-dimensional space due to occlusion and invisibility, respectively. Furthermore, existing 3D human assets remain small-scale, insufficient for large-scale training. To address these challenges, we propose a latent space generation paradigm for 3D human digitization, which involves compressing multi-view images into Gaussians via a UV-structured VAE, along with DiT-based conditional generation, we transform the ill-posed low-to-high-dimensional mapping problem into a learnable distribution shift, which also supports end-to-end inference. In addition, we employ the multi-view optimization approach combined with synthetic data to construct the HGS-1M dataset, which contains $1$ million 3D Gaussian assets to support the large-scale training. Experimental results demonstrate that our paradigm, powered by large-scale training, produces high-quality 3D human Gaussians with intricate textures, facial details, and loose clothing deformation.

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