Motion Vector-Based Frame Generation for Real-Time Rendering
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
The demand for high frame rate rendering is rapidly increasing, especially in the graphics and gaming industries. Although recent learning-based frame interpolation methods have demonstrated promising results, they have not yet achieved the quality required for real-time gaming. High-quality frame interpolation is critical for rendering faster, dynamic motion during gameplay. In graphics, motion vectors are typically favored over optical flow due to their accuracy and efficiency in game engines. However, motion vectors alone are insufficient for frame interpolation, as they lack bilateral motions for the target frame to interpolate and struggle with capturing non-geometric movements. To address this, we propose a novel method that leverages fast, low-cost motion vectors as guiding flows, integrating them into a task-specific intermediate flow estimation process. Our approach employs a combined motion and image context encoder-decoder to produce more accurate intermediate bilateral flows. As a result, our method significantly improves interpolation quality and achieves state-of-the-art performance in rendered content.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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