Diffusion4D is a novel framework for efficient and scalable 4D content generation, addressing the challenges of slow optimization speeds and multi-view inconsistency in existing methods. The framework integrates spatial and temporal consistency into a single 4D-aware video diffusion model, enabling the synthesis of orbital views of dynamic 3D assets. Key contributions include: 1. **4D-Aware Video Diffusion Model**: A model trained on a curated dynamic 3D dataset to generate orbital videos of dynamic 3D assets, mimicking the process of photographing 4D assets. 2. **3D-to-4D Motion Magnitude Metric**: Introduces a metric to control the dynamic strength of 3D assets, with a reconstruction loss to learn 3D-to-4D dynamics. 3. **3D-Aware Classifier-Free Guidance**: Enhances dynamics learning and generation by providing classifier-free guidance during inference. 4. **Coarse-to-Fine 4D Construction**: Uses Gaussian splatting to explicitly construct 4D assets, ensuring spatial-temporal consistency across different views and timestamps. Experiments demonstrate that Diffusion4D outperforms previous methods in terms of generation efficiency and 4D geometry consistency, achieving high-quality and diverse 4D assets within minutes. The framework is versatile, accommodating various prompt modalities such as text, single images, and static 3D content.Diffusion4D is a novel framework for efficient and scalable 4D content generation, addressing the challenges of slow optimization speeds and multi-view inconsistency in existing methods. The framework integrates spatial and temporal consistency into a single 4D-aware video diffusion model, enabling the synthesis of orbital views of dynamic 3D assets. Key contributions include: 1. **4D-Aware Video Diffusion Model**: A model trained on a curated dynamic 3D dataset to generate orbital videos of dynamic 3D assets, mimicking the process of photographing 4D assets. 2. **3D-to-4D Motion Magnitude Metric**: Introduces a metric to control the dynamic strength of 3D assets, with a reconstruction loss to learn 3D-to-4D dynamics. 3. **3D-Aware Classifier-Free Guidance**: Enhances dynamics learning and generation by providing classifier-free guidance during inference. 4. **Coarse-to-Fine 4D Construction**: Uses Gaussian splatting to explicitly construct 4D assets, ensuring spatial-temporal consistency across different views and timestamps. Experiments demonstrate that Diffusion4D outperforms previous methods in terms of generation efficiency and 4D geometry consistency, achieving high-quality and diverse 4D assets within minutes. The framework is versatile, accommodating various prompt modalities such as text, single images, and static 3D content.