Image Conductor: Precision Control for Interactive Video Synthesis
This addresses the challenge of labor-intensive video production for filmmaking and animation by enabling interactive video asset generation, though it appears incremental as it builds on existing generative AI techniques.
The paper tackles the problem of achieving precise control over camera transitions and object movements in interactive video synthesis from a single image, resulting in a method that demonstrates precision and fine-grained control in generating motion-controllable videos.
Filmmaking and animation production often require sophisticated techniques for coordinating camera transitions and object movements, typically involving labor-intensive real-world capturing. Despite advancements in generative AI for video creation, achieving precise control over motion for interactive video asset generation remains challenging. To this end, we propose Image Conductor, a method for precise control of camera transitions and object movements to generate video assets from a single image. An well-cultivated training strategy is proposed to separate distinct camera and object motion by camera LoRA weights and object LoRA weights. To further address cinematographic variations from ill-posed trajectories, we introduce a camera-free guidance technique during inference, enhancing object movements while eliminating camera transitions. Additionally, we develop a trajectory-oriented video motion data curation pipeline for training. Quantitative and qualitative experiments demonstrate our method's precision and fine-grained control in generating motion-controllable videos from images, advancing the practical application of interactive video synthesis. Project webpage available at https://liyaowei-stu.github.io/project/ImageConductor/