TY - GEN
T1 - Multi-Object Sketch Animation with Grouping and Motion Trajectory Priors
AU - Liang, Guotao
AU - Hu, Juncheng
AU - Xing, Ximing
AU - Zhang, Jing
AU - Yu, Qian
N1 - Publisher Copyright:
© 2025 ACM.
PY - 2025/10/27
Y1 - 2025/10/27
N2 - We introduce GroupSketch, a novel method for vector sketch animation that effectively handles multi-object interactions and complex motions. Existing approaches struggle with these scenarios, either being limited to single-object cases or suffering from temporal inconsistency and poor generalization. To address these limitations, our method adopts a two-stage pipeline comprising Motion Initialization and Motion Refinement. In the first stage, the input sketch is interactively divided into semantic groups and key frames are defined, enabling the generation of a coarse animation via interpolation. In the second stage, we propose a Group-based Displacement Network (GDN), which refines the coarse animation by predicting group-specific displacement fields, leveraging priors from a text-to-video model. GDN further incorporates specialized modules, such as Context-conditioned Feature Enhancement (CCFE), to improve temporal consistency. Extensive experiments demonstrate that our approach significantly outperforms existing methods in generating high-quality, temporally consistent animations for complex, multi-object sketches, thus expanding the practical applications of sketch animation.
AB - We introduce GroupSketch, a novel method for vector sketch animation that effectively handles multi-object interactions and complex motions. Existing approaches struggle with these scenarios, either being limited to single-object cases or suffering from temporal inconsistency and poor generalization. To address these limitations, our method adopts a two-stage pipeline comprising Motion Initialization and Motion Refinement. In the first stage, the input sketch is interactively divided into semantic groups and key frames are defined, enabling the generation of a coarse animation via interpolation. In the second stage, we propose a Group-based Displacement Network (GDN), which refines the coarse animation by predicting group-specific displacement fields, leveraging priors from a text-to-video model. GDN further incorporates specialized modules, such as Context-conditioned Feature Enhancement (CCFE), to improve temporal consistency. Extensive experiments demonstrate that our approach significantly outperforms existing methods in generating high-quality, temporally consistent animations for complex, multi-object sketches, thus expanding the practical applications of sketch animation.
KW - interactive vector animation
KW - multi-object sketch animation
KW - text-guided animation generation
UR - https://www.scopus.com/pages/publications/105024079436
U2 - 10.1145/3746027.3754502
DO - 10.1145/3746027.3754502
M3 - 会议稿件
AN - SCOPUS:105024079436
T3 - MM 2025 - Proceedings of the 33rd ACM International Conference on Multimedia, Co-Located with MM 2025
SP - 9237
EP - 9246
BT - MM 2025 - Proceedings of the 33rd ACM International Conference on Multimedia, Co-Located with MM 2025
PB - Association for Computing Machinery, Inc
T2 - 33rd ACM International Conference on Multimedia, MM 2025
Y2 - 27 October 2025 through 31 October 2025
ER -