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arXiv · 2506.07698

NOVA3D: Normal Aligned Video Diffusion Model for Single Image to 3D Generation

Yuxiao Yang·Peihao Li·Yuhong Zhang·Junzhe Lu·Xianglong He·Minghan Qin·Weitao Wang·Haoqian Wang
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Abstract

3D AI-generated content (AIGC) has made it increasingly accessible for anyone to become a 3D content creator. While recent methods leverage Score Distillation Sampling to distill 3D objects from pretrained image diffusion models, they often suffer from inadequate 3D priors, leading to insufficient multi-view consistency. In this work, we introduce NOVA3D, an innovative single-image-to-3D generation framework. Our key insight lies in leveraging strong 3D priors from a pretrained video diffusion model and integrating geometric information during multi-view video fine-tuning. To facilitate information exchange between color and geometric domains, we propose the Geometry-Temporal Alignment (GTA) attention mechanism, thereby improving generalization and multi-view consistency. Moreover, we introduce the de-conflict geometry fusion algorithm, which improves texture fidelity by addressing multi-view inaccuracies and resolving discrepancies in pose alignment. Extensive experiments validate the superiority of NOVA3D over existing baselines.

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Yuxiao Yang, Peihao Li, Yuhong Zhang, Junzhe Lu, Xianglong He, Minghan Qin, Weitao Wang, Haoqian Wang. 2025-06-09. NOVA3D: Normal Aligned Video Diffusion Model for Single Image to 3D Generation. https://arxiv.org/abs/2506.07698

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