Multi-Object Sketch Animation with Grouping and Motion Trajectory Priors
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 37 of its 61 references that are in the map. First posted on arXiv 21 Aug 2025.
Builds on
37 of 61 references.
References · 37
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25 more
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Built on by
Citing papers per year: 2025: 1; 2026: 8.
9 papers.
- 2026DriveVA: Video Action Models are Zero-Shot Driverscited by 23
- 2026Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedbackcited by 12
- 2026MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolutioncited by 12
- 2026Gated Relational Alignment via Confidence-based Distillation for Efficient VLMscited by 6
- 2026VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animationcited by 6
- 2026Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learningcited by 4
- 2026MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentationcited by 4
- 2026Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimizationcited by 1
- 2025Vector sketch animation generation with differentiable motion trajectoriescited by 0
- 2026MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation19 Jul 2026
- 2026Sketch2Motion: Text-driven 2D Sketch to 3D Animation via Diffusion-guided Skeleton Optimization27 May 2026
- 2026VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation2 May 2026
- 2026MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution29 Apr 2026
- 2026Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback22 Apr 2026
- 2026DriveVA: Video Action Models are Zero-Shot Drivers5 Apr 2026
- 2026Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs30 Jan 2026
- 2026Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning27 Jan 2026
- 2025Vector sketch animation generation with differentiable motion trajectories30 Sep 2025