CollageVis: Rapid Previsualization Tool for Indie Filmmaking using Video Collages
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
Previsualization, previs, is essential for film production, allowing cinematographic experiments and effective collaboration. However, traditional previs methods like 2D storyboarding and 3D animation require substantial time, cost, and technical expertise, posing challenges for indie filmmakers. We introduce CollageVis, a rapid previsualization tool using video collages. CollageVis enables filmmakers to create previs through two main user interfaces. First, it automatically segments actors from videos and assigns roles using name tags, color filters, and face swaps. Second, it positions video layers on a virtual stage and allows users to record shots using mobile as a proxy for a virtual camera. These features were developed based on formative interviews by reflecting indie filmmakers’ needs and working methods. We demonstrate the system’s capability by replicating seven film scenes and evaluate the system’s usability with six indie filmmakers. The findings indicate that CollageVis allows more flexible yet expressive previs creation for idea development and collaboration.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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