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Computational Pathology Reading Roadmap

Writing study: MaskControl: Spatio-Temporal Control for Masked Motion Synthesis

← ICCV 2025 oral writing corpus

Ekkasit Pinyoanuntapong, Muhammad Saleem, Korrawe Karunratanakul, Pu Wang, Hongfei Xue, Chen Chen, Chuan Guo, Junli Cao, Jian Ren, Sergey Tulyakov · Official ICCV 2025 oral record

Figure, table and section ledger

Editorial contribution type: Architecture & representation (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
11 physical pages
Before references / appendix
≈ 8.05 page-equivalents
Main figures / tables
4 / 5
Attached appendix figures / tables
0 / 0
Attached appendix span
No boundary detected; not assumed zero
Before references / appendix: ≈ 73.18%References / intervening material: ≈ 26.82%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Extraction notes: No attached appendix boundary detected. Zero means no recognized caption, not independently verified absence.

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 headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction1≈ 0.95 pp.8.64%
2. Related Work2≈ 0.55 pp.5%
3. MaskControl3≈ 2.61 pp.23.73%
4. Applications5≈ 0.54 pp.4.91%
5. Experiment6≈ 1.74 pp.15.82%
6. Ablation Study8≈ 0.64 pp.5.82%
7. Conclusion8≈ 0.24 pp.2.18%
References9≈ 2.95 pp.26.82%
All detected figure and table locations

Main text

Attached appendix

    No recognized caption labels.

    Argument and organization

    1. 1 Introduction; 2 Related Work

      Separates sparse/dense spatial control, motion quality and sampling efficiency as concurrent requirements.

    2. 3 MaskControl; 4 Applications

      Defines tokenization, training-time regularization, inference optimization and differentiable sampling before three control use cases.

    3. 5 Experiment; 6 Ablation Study; 7 Conclusion

      Separates pelvis/all-joint training, upper-body editing, unseen objectives and control-density sensitivity.

    Problem, claim and contribution

    Precision and generative quality are treated as separate objectives; the opening comparison also distinguishes which baselines can control which joints.

    Table 1 and Sections 1–3, physical pp.1–3 · Main paper

    Argument and section order

    Training-time logits regularization and test-time logits optimization have distinct subsections, preventing the runtime control procedure from being confused with learned conditioning.

    Sections 3.2–3.4, pp.3–5 · Main paper

    Figures, captions and visual explanation

    A four-part architecture diagram includes tokenizer, differentiable-sampling inset, training and inference paths, with frozen/trainable and codebook/token legends.

    Figure 2, p.4 · Main paper

    Tables, comparisons and evidence

    The main table splits pelvis-only from all-random-joint training, defines centimetre errors and the 50cm failure threshold, and uses a target-value arrow for diversity rather than higher-is-better.

    Table 2, p.7 · Main paper

    Appendix and reproducibility

    The joint-signal description flags zero-coordinate ambiguity and points to Section A.11; cross-joint combinations point to Table 7 beyond the main table sequence. Only main pointers were inspected.

    Section 3.2, p.4; Section 5.3, p.7 · Main paper

    Clarity and writing conventions

    The control-density ablation explicitly distinguishes counts of controlled frames from percentages of total sequence length, avoiding misleading uniform units.

    Section 6.2 and Table 5, p.8 · Main paper

    What to reuse—and what not to infer

    Transferable lesson: Separate training privileges, inference optimization and metric thresholds when reporting controllable generation.

    Boundary: Zero-shot objective control still performs inference-time optimization using specified losses; it is not unrestricted physical understanding. Low joint error and FID do not prove biomechanical validity, collision freedom or generalization outside HumanML3D.

    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-oriented scan across the main manuscript, physical PDF pp.1–8, covering framing, section progression, method explanations, captions, comparison protocols and concluding scope; reference boundary checked. Selected full-page rasters were inspected. Not exhaustive line-by-line reading, proof verification or experimental reproduction.
    Appendix / supplement
    Main-paper appendix/supplement references were checked. A separate supplementary source is linked in the inventory but was not retrieved or inspected; its contents are not included in the reading or visual-page counts.
    Visually inspected PDF pages
    4, 7

    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.