  Current 4D generation methods have achieved noteworthy efficacy with the aid
of advanced diffusion generative models. However, these methods lack multi-view
spatial-temporal modeling and encounter challenges in integrating diverse prior
knowledge from multiple diffusion models, resulting in inconsistent temporal
appearance and flickers. In this paper, we propose a novel 4D generation
pipeline, namely 4Diffusion aimed at generating spatial-temporally consistent
4D content from a monocular video. We first design a unified diffusion model
tailored for multi-view video generation by incorporating a learnable motion
module into a frozen 3D-aware diffusion model to capture multi-view
spatial-temporal correlations. After training on a curated dataset, our
diffusion model acquires reasonable temporal consistency and inherently
preserves the generalizability and spatial consistency of the 3D-aware
diffusion model. Subsequently, we propose 4D-aware Score Distillation Sampling
loss, which is based on our multi-view video diffusion model, to optimize 4D
representation parameterized by dynamic NeRF. This aims to eliminate
discrepancies arising from multiple diffusion models, allowing for generating
spatial-temporally consistent 4D content. Moreover, we devise an anchor loss to
enhance the appearance details and facilitate the learning of dynamic NeRF.
Extensive qualitative and quantitative experiments demonstrate that our method
achieves superior performance compared to previous methods.
