Source-linked AI summary
SketchDynamics: Exploring Free-Form Sketches for Dynamic Intent Expression in Animation Generation
Boyu Li, Lin-Ping Yuan, Zeyu Wang, Hongbo Fu
TL;DR
Existing sketch-based animation methods often restrict sketches to predefined forms or commands, limiting free-form expression of dynamic intent. SketchDynamics introduces an open-ended sketch-to-motion-graphics workflow with VLM interpretation, adaptive clarification, and iterative refinement. A three-stage study reports that sketches can express animation intent with minimal interaction, while clarification helps address ambiguity and user intervention shapes the result.
Problem
Existing approaches often constrain sketches through predefined visual forms or fixed commands, limiting their expressive potential for dynamic intent.
Method
SketchDynamics uses free-form storyboard sketches as open-ended prompts for VLM-assisted generation of explainer-style motion graphics, with clarification and multimodal refinement.
Results
A three-stage study found that free-form sketches can convey animation intent with minimal, intuitive input, while clarification supports interpretation and intent shaping.
Takeaways & Limitations
The paradigm positions sketch ambiguity, VLM interpretation, and user intervention as collaborative parts of dynamic content creation.
Takeaways & Limitations
The study is limited by homogeneous participants, delayed rendering, dependence on general-purpose VLMs, and a focus on short motion-graphics storyboards.
Abstract
from arXiv · showhide
Sketching provides an intuitive way to convey dynamic intent in animation authoring (i.e., how elements change over time and space), making it a natural medium for automatic content creation. Yet existing approaches often constrain sketches to fixed command tokens or predefined visual forms, overlooking their freeform nature and the central role of humans in shaping intention. To address this, we introduce an interaction paradigm where users convey dynamic intent to a vision-language model via free-form sketching, instantiated here in a sketch storyboard to motion graphics workflow. We implement an interface and improve it through a three-stage study with 24 participants. The study shows how sketches convey motion with minimal input, how their inherent ambiguity requires users to be involved for clarification, and how sketches can visually guide video refinement. Our findings reveal the potential of sketch and AI interaction to bridge the gap between intention and outcome, and demonstrate its applicability to 3D animation and video generation.
SketchDynamics
SketchDynamics combines storyboard sketching, clarification, asset provision, and iterative refinement to produce a traffic-light animation sequence.
- Users sketch a traffic-light storyboard showing red stop, yellow flashing, and green go.
- The system asks clarifying questions about timing and assets, which users answer by selecting options or uploading files.
- Users refine generated frames by specifying repeated flashing and sketching a velocity–time curve for the car.
- Final video clips illustrate the produced animation sequence.
CCS Concepts
The paper is situated in human-centered computing, specifically interactive systems and tools.
- The work falls under human-centered computing, including interactive systems and tools.
Keywords
The paper focuses on free-form sketches, dynamic intention, and creativity support in animation generation.
- The keywords identify free-form sketching, dynamic intention, and creativity support as the paper’s central themes.
- The work concerns animation generation, as indicated by its publication title.
1 Introduction
Sketching offers an intuitive way to express dynamic intent, but existing systems often constrain sketches or leave their interpretation ambiguous. SketchDynamics instead treats sketches as open-ended prompts and studies how VLM interpretation and user intervention can support animation generation.
- Storyboards depict keyframes and narrative flow, while free-form annotations use marks such as arrows and dashed lines to express dynamic intent.
- Existing sketch-based systems support video generation and incremental authoring but often constrain sketches through stroke recognition or predefined commands.
- SketchDynamics investigates whether free-form sketches can capture dynamic intent, using VLMs to interpret abstract sketches for motion-graphics authoring.
- A three-stage study examines free-form storyboard generation, ambiguity-matched clarification, and user intervention during dynamic content creation.
- The paper prioritizes restoring sketches’ expressive freedom as casual, intuitive drawings rather than pursuing high-quality video results.
- The proposed paradigm treats sketches as open-ended prompts shaped jointly by ambiguity, VLM interpretation, and user intervention.
2 Related Work
Sketch research has progressed from fixed symbol-to-command mappings toward inferring higher-level authoring intent, but free-form interpretation remains ambiguous. SketchDynamics addresses this gap by using unconstrained storyboard sketches as adaptable prompts for motion-graphics generation and iterative refinement.
- Sketch systems have supported rapid prototyping and authoring by treating sketches as intuitive, fluid representations of user ideas.
- Earlier systems recognized strokes or sketches as predefined symbols and mapped them to specific operations or animation commands.
- More recent systems infer higher-level intent, with vision–language models interpreting abstract sketches and generating or editing content beyond simple classification.
- Free-form interpretation remains difficult because sketches are inherently ambiguous, motivating user participation in co-interpretation rather than fixed command vocabularies.
- Constrained sketch representations limit motion-graphics authoring, whereas free-form storyboards support open-ended spatial, temporal, and stylistic cues followed by iterative refinement.
- Existing motion-graphics systems demonstrate AI reasoning about temporal properties and vector assets, but a gap remains in using informal sketches to express dynamic intent throughout authoring.
3 SketchDynamics
SketchDynamics investigates free-form storyboard sketches as a medium for generating dynamic content through a three-stage interaction process. The study examines direct interpretation, user clarification of ambiguity, and contextual iterative refinement.
- SketchDynamics is a proof-of-concept system that studies whether free-form sketches can reliably communicate animation ideas during authoring.
- The investigation uses three stages: direct sketch-to-video generation, clarification cues for ambiguous interpretations, and frame-based iterative editing with contextual feedback.
- Stage 1: Understanding sketch and interpretation: Stage 1 examines how users naturally express animation intent and how a unified interface converts storyboard sketches into motion-graphics videos.
- Stage 1: Understanding sketch and interpretation: Participants used diverse, abstract sketches for rapid prototyping; the VLM interpreted many inputs but often failed when sketches were ambiguous.
- Stages 2–3: Clarification and iterative editing: Stage 2 introduces clarification cues because sketches such as arrows can support multiple interpretations, while Stage 3 addresses ambiguity that becomes apparent only after video generation.
4 Stage 1: Initial Sketch to Animation Authoring
Stage 1 studies how participants use free-form storyboards to express animation intent and how those sketches become preliminary videos. The findings show efficient but ambiguous communication, with interpretation favoring semantic motion over literal geometry.
- The baseline study observed sketching behaviors and interpretation patterns rather than optimizing output quality, using results to guide subsequent investigations.
- The interface connected freehand sketching, storyboard management, lightweight editing, and generated motion-graphics previews.
- Compositing all sketches and text scripts into one storyboard produced more coherent and temporally consistent animations than processing each sketch individually.
- 8 participants completed an approximately 30-minute session involving three sketch storyboards, three generated-video reviews, interviews, and questionnaires.
- Participants created concise explainer storyboards with basic shapes so they could focus on dynamic logic such as causality and flow rather than static aesthetics.
- Free-form sketches expressed animation intent through diverse conventions including arrows, onion skinning, and index numbers, but these conventions were idiosyncratic and context-dependent.
- 6 out of 8 participants initially doubted that highly abstract drawings could be understood, although many abstract sketches were interpreted meaningfully.
- The VLM often prioritized semantic intent over literal geometric fidelity, producing cleaned-up or canonical versions of rough participant sketches.
5 Stage 2: Clarification Guide Interpretation
Stage 2 treats sketch ambiguity as a condition to manage rather than eliminate, using adaptive clarification cues to preserve freehand expression while involving users in interpretation. Across 24 attempts, cues improved alignment for most outputs and were generally viewed as useful, though physics-inspired cases remained difficult.
- Challenges: Participants’ abstract marks efficiently conveyed motion but often left duration, scale, speed, or intended action unspecified to other viewers and the VLM.The study identified ambiguity both between user intention and sketch production and between sketches and machine interpretation.
- Clarification Cue: The system maps ambiguity levels to interventions ranging from quick confirmation and multiple choice to parameter entry or text/asset upload.This layered strategy preserves freehand sketching while requesting only the information needed for the detected uncertainty.
- Results: 87 clarification cues were collected across 24 creation attempts, with multiple choice used most often because sketches frequently admitted several plausible interpretations.Quick confirmations and fill-value requests were less frequent, while text/upload cues addressed highly abstract or symbolic sketches.
- User Reactions to Cue: Cue demand varied with sketch abstraction, ranging from almost no intervention to 5–6 cues within an attempt.Participants described prompts as helpful when unusual or unclear sketches required additional guidance.
- User Reactions to Cue: Seven out of eight participants said cues helped avoid misleading outputs, and participants generally regarded inline checks as non-disruptive opportunities to make intent explicit.Multiple-choice previews were especially necessary for ambiguous arrows and curves, whereas fill-value cues divided opinions.
- Results: 19 out of 24 attempts produced outputs rated closer to the intended animation after clarification, while two physics-inspired attempts remained unsatisfactory.Different cue types addressed timing, semantic assets, and directional intent, often determining whether the result became usable.
6 Stage 3: Refinement with Visual Context
Stage 3 adds direct video refinement through visual keyframe context, allowing users to correct localized animation details without redrawing the storyboard. Participants reported stable unaffected content, greater control, and efficient iteration despite the added interaction.
- Motivation: The prior workflow required storyboard revision and full regeneration, which could alter unrelated content when users intended only a small local change.This unpredictability motivated direct refinement within generated videos.
- Refinement Workflow: The refinement workflow exposes salient video timestamps as keyframes, letting users select a context-rich anchor for targeted visual or textual edits.The VLM analyzes the initial animation code to identify interpretable keyframes.
- Refinement Workflow: User refinements, keyframe timestamps, extracted keyframes, and the initial animation are supplied together so the VLM updates only the relevant code portion.The revised code is rendered into an updated preview for focused iteration.
- Study Results: 55 refinement operations were collected across 12 edited videos, averaging 4.6 refinements per task; 36 of 55 were sketch-based.Sketches primarily supported spatial animations, while text provided a complementary input modality.
- Study Results: In 10 of 12 final outputs, users reported that unaffected video portions remained stable, reducing frustration compared with restarting the storyboard.Participants said this locality preserved creative momentum even though the stage took longer than earlier workflows.
- User Experience: Seven out of eight participants reported stronger control, describing refinement as a shift from one-shot generation toward iterative collaboration.Higher-level requests such as “make this look more natural” were not successfully interpreted.
7 Generalizability and Extensibility
The authors position SketchDynamics as a free-form, low-barrier alternative for expressing dynamic intent and outline extensions beyond 2D motion graphics. The concept combines sketch interpretation with clarification and refinement, including an implemented Unity example for 3D animation.
- Generalizability: The proposed pipeline combines VLM interpretation with clarification and refinement cues and is presented as extensible to other AI-generated dynamic content.The broader scenarios are design outlooks rather than complete implemented systems.
- Video Generation: Unlike storyboard systems requiring detailed keyframe layouts and extensive scripts, SketchDynamics emphasizes minimal sketches for abstract concepts.The authors frame this contrast as lowering the entry barrier for users with less drawing expertise.
- Video Generation: Figure 9 illustrates refinement by selecting a representative keyframe, adding an arrow for Earth’s orbital motion, and regenerating the updated video.The workflow updates the underlying code to produce the edited motion.
- Video Generation: The concept supports communicating intent before generation through iterative back-and-forth interaction from an abstract sketch drawn from scratch.This differs from draw-to-video interaction that annotates existing images.
- 3D Animation: A Unity extension demonstrates 3D animation authoring with sketches of cubes and a wall, using arrows to indicate motion and collision.The example supplements sketches with additional attributes for a 3D dynamic scene.
8 Discussion
Across three stages, SketchDynamics shows that users’ animation intent evolves through interaction: free-form sketches offer expressive, low-effort input, while ambiguity makes clarification and refinement necessary. The resulting workflow progressively formalizes intent through semantic disambiguation before generation and perceptual adjustment afterward.
- Clarification cues resolve high-level structural ambiguities before generation, whereas refinement cues adjust local perceptual details such as timing and color afterward.Together they support a “disambiguate-then-refine” workflow and defer precise specification until it is useful.
- Users’ animation intent often develops through interaction rather than being fully formed at the outset.
- SketchDynamics extends beyond motion graphics to video generation and 3D dynamic scenes, including a block-collision simulation workflow in Unity.
- The iterative co-creative loop lets users continually reinterpret sketches and reshape outputs instead of relying on one-shot generation.
- Free-form sketches communicate rough shapes, motion directions, and symbolic marks with minimal input, while precise paths and data diagrams provide finer control when needed.The system interpreted high-level semantic sketches more consistently than low-level details.
- Users express motion either through in-image annotations such as arrows and motion curves or through successive storyboard keyframes that encode temporal evolution.
- Current sketch interaction remains limited by ambiguity, pixel-level VLM parsing, and inconsistent interpretation of stroke roles and low-level structure.The system cannot fully exploit structural and temporal information embedded in stroke data.
9 Conclusion
SketchDynamics uses a three-stage feedback loop to turn informal sketch storyboards into motion graphics while progressively addressing interpretation ambiguity and output mismatch. Across the stages, clarification and refinement improved alignment and control without reducing perceived ease or flow.
- The three-stage workflow progresses from free-form sketch-to-motion-graphics generation, to clarification of uncertainty, to visual post-editing for closing intention–outcome gaps.
- Alignment and Control shifted from predominantly negative ratings in Stage 1 to highly positive ratings in Stage 3.
- Effort ratings improved over Stage 1 despite added interaction steps, while Flow and Ease remained consistently high.The authors attribute this to targeted refinement being less laborious than repetitive trial-and-error redrawing.
B Visual Understanding Capability Experiment
The appendix documents the pilot and study instruments used to examine VLM interpretation of free-form sketches and participants’ experiences across three stages. It includes the feasibility checks, participant background measures, Likert ratings, and qualitative interview questions.
- Pilot tests examined prompt design and the VLM’s ability to interpret abstract free-form sketches, with encouraging feasibility results.
- Stage 1 examples illustrate how prompt-engineered VLM interpretation mapped rough or symbolic sketches into animation concepts.
- The study instruments included participant background surveys, post-task Likert questionnaires, and semi-structured interview guides.
- Background measures covered demographics, motion-graphics experience, video-editing and animation proficiency, sketching frequency, and drawing comfort.
- Participants rated Alignment, Ease, Flow, Control, Effort, and Explore after each stage on a 7-point Likert scale.
- Qualitative questions examined sketching strategies, difficult-to-express intents, interpretation surprises, clarification requests, and frame- or text-based refinement.
- The appendix includes a table of sketch types and the animation intentions they convey.