  Recent progress in pre-trained diffusion models and 3D generation have
spurred interest in 4D content creation. However, achieving high-fidelity 4D
generation with spatial-temporal consistency remains a challenge. In this work,
we propose STAG4D, a novel framework that combines pre-trained diffusion models
with dynamic 3D Gaussian splatting for high-fidelity 4D generation. Drawing
inspiration from 3D generation techniques, we utilize a multi-view diffusion
model to initialize multi-view images anchoring on the input video frames,
where the video can be either real-world captured or generated by a video
diffusion model. To ensure the temporal consistency of the multi-view sequence
initialization, we introduce a simple yet effective fusion strategy to leverage
the first frame as a temporal anchor in the self-attention computation. With
the almost consistent multi-view sequences, we then apply the score
distillation sampling to optimize the 4D Gaussian point cloud. The 4D Gaussian
spatting is specially crafted for the generation task, where an adaptive
densification strategy is proposed to mitigate the unstable Gaussian gradient
for robust optimization. Notably, the proposed pipeline does not require any
pre-training or fine-tuning of diffusion networks, offering a more accessible
and practical solution for the 4D generation task. Extensive experiments
demonstrate that our method outperforms prior 4D generation works in rendering
quality, spatial-temporal consistency, and generation robustness, setting a new
state-of-the-art for 4D generation from diverse inputs, including text, image,
and video.
