Creating flexible 3D scenes from a single image is vital when direct 3D data acquisition is costly or impractical. We introduce NavCrafter, a novel framework that explores 3D scenes from a single image by synthesizing novel-view video sequences with camera controllability and temporal-spatial consistency. NavCrafter leverages video diffusion models to capture rich 3D priors and adopts a geometry-aware expansion strategy to progressively extend scene coverage. To enable controllable multi-view synthesis, we introduce a multi-stage camera control mechanism that conditions diffusion models with diverse trajectories via dual-branch camera injection and attention modulation. We further propose a collision-aware camera trajectory planner and an enhanced 3D Gaussian Splatting (3DGS) pipeline with depth-aligned supervision, structural regularization and refinement. Extensive experiments demonstrate that NavCrafter achieves state-of-the-art novel-view synthesis under large viewpoint shifts and substantially improves 3D reconstruction fidelity.
@article{arxiv.2604.02828,
title = {NavCrafter: Exploring 3D Scenes from a Single Image},
author = {Hongbo Duan and Peiyu Zhuang and Yi Liu and Zhengyang Zhang and Yuxin Zhang and Pengting Luo and Fangming Liu and Xueqian Wang},
journal= {arXiv preprint arXiv:2604.02828},
year = {2026}
}