MoonShot: Towards Controllable Video Generation and Editing with Motion-Aware Multimodal Conditions

Current video diffusion models (VDMs) mostly rely on text conditions, limiting control over video appearance and geometry. This study introduces a new model, MoonShot, conditioning on both image and text for enhanced control. It features the Multimodal Video Block (MVB), integrating the motion-aware dual cross-attention layer for precise appearance and motion alignment with provided prompts, and the spatiotemporal attention layer for large motion dynamics. It can also incorporate pre-trained Image ControlNet modules for geometry conditioning without extra video training. Experiments show our model significantly improves visual quality and motion fidelity, and its versatility allows for applications in personalized video generation, animation, and editing, making it a foundational tool for controllable video creation. More video results can be found here.