Writing study: Motion Attribution for Video Generation
← ICML 2026 oral writing corpus
Xindi Wu, Despoina Paschalidou, Jun Gao, Antonio Torralba, Laura Leal-Taixé, Olga Russakovsky, Sanja Fidler, Jonathan Lorraine · Official ICML 2026 oral record
Figure, table and section ledger
Editorial contribution type: Training, objectives & optimization (primary).
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 22 physical pages
- Before references / appendix
- ≈ 8.19 page-equivalents
- Main figures / tables
- 5 / 2
- Attached appendix figures / tables
- 4 / 5
- Attached appendix span
- ≈ 9.96 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 1 | ≈ 0.85 pp. | 3.86% |
| 2. Background | 2 | ≈ 1.06 pp. | 4.82% |
| 3. Method | 3 | ≈ 2.64 pp. | 12% |
| 4. Experiment | 5 | ≈ 3 pp. | 13.64% |
| 5. Conclusion | 8 | ≈ 0.17 pp. | 0.77% |
| Acknowledgements | 9 | ≈ 0.09 pp. | 0.41% |
| Impact Statement | 9 | ≈ 0.06 pp. | 0.27% |
| References | 9 | ≈ 3.85 pp. | 17.5% |
| A. Notation | 13 | ≈ 2.04 pp. | 9.27% |
| B. Extended Related Work | 15 | ≈ 0.74 pp. | 3.36% |
| C. Additional Experiments | 15 | ≈ 0.73 pp. | 3.32% |
| D. Analysis | 16 | ≈ 1.12 pp. | 5.09% |
| E. Additional Method Details | 17 | ≈ 0.72 pp. | 3.27% |
| F. Additional Experiment Details | 18 | ≈ 2 pp. | 9.09% |
| G. Discussion | 20 | ≈ 0.92 pp. | 4.18% |
| H. Visualization | 21 | ≈ 1.69 pp. | 7.68% |
All detected figure and table locations
Main text
Argument and organization
- Problem and contribution
Identifies compute, variable clip duration and temporal failure modes as distinct obstacles to transferring image attribution into video.
- Argument sequence
Method subsections separately solve estimator scale, duration bias and motion emphasis before selection and explicit computational accounting.
- Evidence and verification
Human comparisons state 50 videos and 17 participants, while projection analysis tests agreement with full gradients rather than treating compression as exact.
Problem, claim and contribution
Identifies compute, variable clip duration and temporal failure modes as distinct obstacles to transferring image attribution into video.
Contributions and background, p. 2 · Main paper
Argument and section order
Method subsections separately solve estimator scale, duration bias and motion emphasis before selection and explicit computational accounting.
Sections 3.2-3.6, pp. 3-5 · Main paper
Figures, captions and visual explanation
The results page combines a normalization example, a projection-dimension tradeoff curve and a human preference table that retains ties and losses; however the normalization caption reverses the panels' visible with/without labels.
Figures 4-5 and Table 2, p. 8; PNG inspected; background p. 2 also visually inspected · Main paper
Tables, comparisons and evidence
Human comparisons state 50 videos and 17 participants, while projection analysis tests agreement with full gradients rather than treating compression as exact.
Sections 4.3-4.4, pp. 7-8 · Main paper
Appendix and reproducibility
Runtime tables separate an approximately 150-hour one-time gradient pass from about 54-second new-query work, and limitations retain camera-motion and guidance mismatches.
Appendix G and Tables 6-7, pp. 20-21 · Main paper
Clarity and writing conventions
The appendix candidly distinguishes amortized usefulness from higher upfront cost; motion-overlay captions call the visualization heuristic intuition rather than causal validation.
Appendix G.1, p. 20; Figure 9, p. 22 · Main paper
What to reuse—and what not to infer
Transferable lesson: For amortized selection methods, report both initialization cost and per-query cost, and keep preference ties and approximation error visible.
Boundary: Motion attribution is an approximate estimator, not exact training-data causality; retain the 10k-sample experiment scope and resolve the Figure 4 left/right caption mismatch before reuse.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-10-07
- Reading status
- Writing-focused manuscript scan + selected visuals inspected
- Main text
- Writing-focused distributed manuscript scan, not exhaustive technical/proof validation. Sections 1-5, pp. 1-8: video-specific attribution obstacles, estimator, duration correction, motion weighting, selection, costs and ablations.
- Appendix / supplement
- Writing-focused appendix scan; code, data and external assets not executed. Appendices A-H, pp. 13-22: notation, related work, additional model results, motion analysis, method/setup, runtime limitations and overlays.
- Visually inspected PDF pages
- 2, 8
This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.