AI-assisted creative tools reduce the effort of authoring content, but delegating creative decisions to AI can make it difficult for authors to understand, correct, or redirect work. We present OmniScribble, a system for animating multi-object sketches that treats object decomposition and motion planning as editable artifacts. Rather than generating animation end-to-end, OmniScribble presents AI proposals at verification checkpoints, where authors can accept, repair, or regenerate them. Both manual and AI-assisted authoring operate on the intermediate representation and rendering pipeline, allowing us to isolate AI delegation. In a within-subject study with eight participants, AI-assisted authoring yielded a median within-participant reduction in completion time of 71.4\%, while perceived control and motion-intent alignment remained high. Interaction traces and interviews show that participants adapted to AI errors by repairing local issues, regenerating mismatches, and accepting proposals. These findings suggest that inspectable, repairable delegation enables generative automation while maintaining control over the outcome.
Hien Trinh, Tan-Hiep To, Dinh-Khoi Vo, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
Counterfactual sketch animation explores how a static sketch evolves under a hypothetical intervention. We present CounterSketch, a physics-grounded framework that transforms natural-language ``What if...?'' queries into physically plausible sketch animations. The framework integrates VLM-guided scene grounding, causal event-chain reasoning, constraint-based trajectory generation, and verification. It models physical transitions to update the scene graph, computes continuous object trajectories, and validates them against physical, geometric, and semantic constraints. The verified trajectories are then projected into object-level motion bounding boxes to guide downstream sketch-video synthesis. Experiments on CounterSketchBench (80 questions) demonstrate that physics-grounded trajectories provide stronger motion guidance than direct VLM predictions.