RealityCanvas: Augmented Reality Sketching for Embedded and Responsive Scribble Animation Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We introduce RealityCanvas, a mobile AR sketching tool that can easily augment real-world physical motion with responsive hand-drawn animation. Recent research in AR sketching tools has enabled users to not only embed static drawings into the real world but also dynamically animate them with physical motion. However, existing tools often lack the flexibility and expressiveness of possible animations, as they primarily support simple line-based geometry. To address this limitation, we explore both expressive and improvisational AR sketched animation by introducing a set of responsive scribble animation techniques that can be directly embedded through sketching interactions: 1) object binding, 2) flip-book animation, 3) action trigger, 4) particle effects, 5) motion trajectory, and 6) contour highlight. These six animation effects were derived from the analysis of 172 existing video-edited scribble animations. We showcase these techniques through various applications, such as video creation, augmented education, storytelling, and AR prototyping. The results of our user study and expert interviews confirm that our tool can lower the barrier to creating AR-based sketched animation, while allowing creative, expressive, and improvisational AR sketching experiences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it