Sketch-Guided Motion Diffusion for Stylized Cinemagraph Synthesis
This work addresses the problem of intuitive control for designers creating stylized cinemagraphs, offering a more detailed alternative to text inputs, though it is incremental as it builds on existing diffusion models.
The paper tackles the challenge of customizing complex flow motions in stylized cinemagraphs by proposing Sketch2Cinemagraph, a sketch-guided framework that generates high-fidelity cinemagraphs from freehand sketches, achieving continuous temporal flow and outperforming state-of-the-art methods in quantitative comparisons.
Designing stylized cinemagraphs is challenging due to the difficulty in customizing complex and expressive flow motions. To achieve intuitive and detailed control of the generated cinemagraphs, freehand sketches can provide a better solution to convey personalized design requirements than only text inputs. In this paper, we propose Sketch2Cinemagraph, a sketch-guided framework that enables the conditional generation of stylized cinemagraphs from freehand sketches. Sketch2Cinemagraph adopts text prompts for initial content generation and provides hand-drawn sketch controls for both spatial and motion cues. The latent diffusion model is adopted to generate target stylized landscape images along with realistic versions. Then, a pre-trained object detection model is utilized to segment and obtain masks for the flow regions. We proposed a novel latent motion diffusion model to estimate the motion field in the fluid regions of the generated landscape images. The input motion sketches serve as the conditions to control the generated vector fields in the masked fluid regions with the prompt. To synthesize the cinemagraph frames, the pixels within fluid regions are subsequently warped to the target locations for each timestep using a frame generator. The results verified that Sketch2Cinemagraph can generate high-fidelity and aesthetically appealing stylized cinemagraphs with continuous temporal flow from intuitive sketch inputs. We showcase the advantages of Sketch2Cinemagraph through quantitative comparisons against the state-of-the-art generation approaches.