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arXiv · 2605.00345

Pose-Aware Diffusion for 3D Generation

Zihan Zhou·Luxi Chen·Jingzhi Zhou·Yuhao Wan·Min Zhao·Baoyu Fan·Chongxuan Li
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Abstract

Generating pose-aligned 3D objects is challenging due to the spatial mismatches and transformation ambiguities inherent in decoupled canonical-then-rotate paradigms. To this end, we introduce Pose-Aware Diffusion (PAD), a novel end-to-end diffusion framework that synthesizes 3D geometry directly within the observation space. By unprojecting monocular depth into a partial point cloud and explicitly injecting it as a 3D geometric anchor, PAD abandons canonical assumptions to enforce rigorous spatial supervision. This native generation intrinsically resolves pose ambiguity, producing high-fidelity pose-aligned assets. Extensive experiments demonstrate that PAD achieves superior geometric alignment and image-to-3D correspondence compared to state-of-the-art methods. Additionally, PAD naturally extends to compositional 3D scene reconstruction via a simple union of independently generated objects, highlighting its robust ability to preserve precise spatial layouts.

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Zihan Zhou, Luxi Chen, Jingzhi Zhou, Yuhao Wan, Min Zhao, Baoyu Fan, Chongxuan Li. 2026-05-01. Pose-Aware Diffusion for 3D Generation. https://arxiv.org/abs/2605.00345

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