SketchDynamics: Exploring Free-Form Sketches for Dynamic Intent Expression in Animation Generation

Paper Title

SketchDynamics: Exploring Free-Form Sketches for Dynamic Intent Expression in Animation Generation

Publication Info

• Topic area: Free-form sketching as a medium for dynamic intent expression in animation generation.
• Keywords: Free-form sketches, animation generation, vision–language models, dynamic intent, motion graphics, storyboard, user study, clarification cues, refinement cues, human-AI collaboration.

Background and Problem

• Problem / challenge: Existing systems constrain sketches to predefined forms or commands, limiting their expressive potential for dynamic intent. They fail to fully leverage the flexibility and ambiguity of free-form sketches in animation creation.
• Significance: Free-form sketches are intuitive and accessible, making them an ideal medium for non-experts to express animation ideas. Addressing their limitations can democratize animation creation and improve human-AI collaboration.
• Motivation and related work: Prior research has explored sketch-based animation but often relies on rigid mappings or constrained formats. Vision–language models (VLMs) offer new opportunities for interpreting abstract sketches, but their application to dynamic content creation remains underexplored. This paper builds on these advancements to investigate how free-form sketches can drive animation workflows.

Solution

• Proposed approach: SketchDynamics, a system that uses free-form sketch storyboards to generate motion graphics, supported by clarification and refinement cues for iterative user interaction.
• Novelty:
  1. A paradigm where sketches serve as open-ended prompts, leveraging ambiguity, VLM interpretation, and user intervention to shape dynamic content.
  2. Introduction of clarification cues to resolve sketch ambiguities and refinement cues for post-generation iterative editing.
  3. Validation of the approach through a three-stage user study, demonstrating its effectiveness in supporting diverse sketches and enabling precise dynamic intent articulation.
• Procedure and key techniques:
  • Stage 1: Users create free-form sketch storyboards, which are interpreted by a VLM to generate motion graphics.
  • Stage 2: Clarification cues address sketch ambiguities via lightweight user interactions (e.g., confirmations, multiple-choice, value inputs).
  • Stage 3: Refinement cues allow users to edit generated videos directly by sketching on keyframes or providing textual prompts, enabling localized adjustments.

Results

• Concrete findings:
  • Stage 1: 24 attempts revealed diverse sketching strategies but highlighted issues with ambiguity and misalignment between intent and output.
  • Stage 2: 87 clarification cues were used across 24 tasks, improving alignment in 19 cases and reducing misinterpretation.
  • Stage 3: 55 refinement operations across 12 videos enabled efficient, localized edits, with users performing an average of 4.6 refinements per task.
• Advantage over baselines:
  • Clarification cues reduced ambiguity and improved alignment between sketches and animations.
  • Refinement cues enabled iterative, localized edits, avoiding the inefficiency of redrawing entire storyboards.
  • Users reported higher alignment, control, and ease of use compared to one-shot generation workflows.
• Experiments / evaluation:
  • Three-stage user study with 24 participants (8 per stage), involving tasks to create explainer-style animations.
  • Metrics included user ratings (alignment, ease, flow, control, effort, exploration) and qualitative feedback.
  • Diverse animation intents were tested, including motion trajectories, transformations, and symbolic annotations.
• Limitations and future work:
  • Homogeneous participant pool (university students) limits generalizability.
  • Lack of real-time feedback during sketching.
  • Dependence on general-purpose VLMs, which may not fully capture animation-specific nuances.
  • Current scope limited to short motion graphics; future work could explore longer or more complex animations and extend to 3D dynamic scenes.

Summary

SketchDynamics introduces a novel approach to animation generation using free-form sketches as dynamic intent expressions. By integrating clarification and refinement cues, the system addresses sketch ambiguity and enables iterative, user-guided refinement of generated animations. A three-stage user study validated the system’s effectiveness, showing improved alignment, control, and creative exploration compared to traditional workflows. While limited to short motion graphics and constrained by current VLM capabilities, the approach demonstrates significant potential for democratizing animation creation and fostering human-AI collaboration. Future work could expand its scope to more complex animations and other dynamic content domains.