Continuous conditional video synthesis by neural processes
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
Different conditional video synthesis tasks, such as frame interpolation and future frame prediction, are typically addressed individually by task-specific models, despite their shared underlying characteristics. Additionally, most conditional video synthesis models are limited to discrete frame generation at specific integer time steps. This paper presents a unified model that tackles both challenges simultaneously. We demonstrate that conditional video synthesis can be formulated as a neural process, where input spatio-temporal coordinates are mapped to target pixel values by conditioning on context spatio-temporal coordinates and pixel values. Our approach leverages a Transformer-based non-autoregressive conditional video synthesis model that takes the implicit neural representation of coordinates and context pixel features as input. Our task-specific models outperform previous methods for future frame prediction and frame interpolation across multiple datasets. Importantly, our model enables temporal continuous video synthesis at arbitrary high frame rates, outperforming the previous state-of-the-art. The source code and video demos for our model are available at https://npvp.github.io .
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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