We present Free4D, a novel tuning-free framework for 4D scene generation from a single image. Existing methods either focus on object-level generation, making scene-level generation infeasible, or rely on large-scale multi-view video datasets for expensive training, with limited generalization ability due to the scarcity of 4D scene data. In contrast, our key insight is to distill pre-trained foundation models for consistent 4D scene representation, which offers promising advantages such as efficiency and generalizability. 1) To achieve this, we first animate the input image using image-to-video diffusion models followed by 4D geometric structure initialization. 2) To turn this coarse structure into spatial-temporal consistent multiview videos, we design an adaptive guidance mechanism with a point-guided denoising strategy for spatial consistency and a novel latent replacement strategy for temporal coherence. 3) To lift these generated observations into consistent 4D representation, we propose a modulation-based refinement to mitigate inconsistencies while fully leveraging the generated information. The resulting 4D representation enables real-time, controllable rendering, marking a significant advancement in single-image-based 4D scene generation.
View on arXiv@article{liu2025_2503.20785,
title={ Free4D: Tuning-free 4D Scene Generation with Spatial-Temporal Consistency },
author={ Tianqi Liu and Zihao Huang and Zhaoxi Chen and Guangcong Wang and Shoukang Hu and Liao Shen and Huiqiang Sun and Zhiguo Cao and Wei Li and Ziwei Liu },
journal={arXiv preprint arXiv:2503.20785},
year={ 2025 }
}