Papers
arxiv:2406.15339

Image Conductor: Precision Control for Interactive Video Synthesis

Published on Jun 21
· Submitted by Liangbin on Jun 26
Authors:
,
,

Abstract

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/

Community

Paper author Paper submitter
Paper author

Code: https://github.com/liyaowei-stu/ImageConductor
The code will be open source soon.

Paper author

Sign up or log in to comment

Models citing this paper 1

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2406.15339 in a dataset README.md to link it from this page.

Spaces citing this paper 1

Collections including this paper 1