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Writing study: DPoser-X: Diffusion Model as Robust 3D Whole-body Human Pose Prior

← ICCV 2025 oral writing corpus

Junzhe Lu, Jing Lin, Hongkun Dou, Ailing Zeng, Yue Deng, Xian Liu, Zhongang Cai, Lei Yang, Yulun Zhang, Haoqian Wang, Ziwei Liu · Official ICCV 2025 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
10 physical pages
Before references / appendix
≈ 8.15 page-equivalents
Main figures / tables
8 / 11
Attached appendix figures / tables
0 / 0
Attached appendix span
No boundary detected; not assumed zero
Before references / appendix: ≈ 81.5%References / intervening material: ≈ 18.5%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≈ 0.59 pp.5.9%
2. Methodology2≈ 2.91 pp.29.1%
3. Experiments5≈ 3.55 pp.35.5%
4. Conclusion8≈ 0.24 pp.2.4%
5. Acknowledgments9≈ 0.1 pp.1%
References9≈ 1.85 pp.18.5%
All detected figure and table locations

Attached appendix

    No recognized caption labels.

    Argument and organization

    1. 1 Introduction; 2 Methodology

      Defines an unconditional pose prior, task-specific inverse problems and a simple denoising regularizer before whole-body extension.

    2. 2.4–2.5 Scheduling and whole-body prior

      Motivates truncated noise schedules and separates part-specific, fused and mixed-data models.

    3. 3 Experiments; 4 Conclusion

      Organizes body, hand/face and whole-body tasks separately, then ablates scheduling and mixed training.

    Problem, claim and contribution

    The prior is reusable across tasks through test-time optimization, with a different measurement loss for each inverse problem rather than one universal predictor.

    Sections 1–2.3, physical pp.1–3 · Main paper

    Argument and section order

    A worked regularization derivation and optimization algorithm precede the whole-body model, separating inference procedure from mixed-data training.

    Section 2.3 and Algorithm 1, pp.3–4 · Main paper

    Figures, captions and visual explanation

    The mixed-training diagram distinguishes unavailable parts, which receive no loss, from deliberately masked whole-body parts, which remain supervised.

    Figure 4, p.5 · Main paper

    Tables, comparisons and evidence

    Pose-generation results explicitly show that high pairwise diversity can accompany implausible samples; completion reports min/mean/std over ten hypotheses rather than only best-case error.

    Table 1 and Figure 5, p.5; Table 7 and Section 3.4, p.7 · Main paper

    Appendix and reproducibility

    Metrics, runtime, baseline implementations, extra tasks, rotation representation and alternate inverse solvers are delegated to supplementary material; only main pointers were inspected.

    Sections 3.1 and 3.5, pp.5,8 · Main paper

    Clarity and writing conventions

    The text notes that the best timestep range depends on task noise, qualifying the appeal of one fixed scheduling recipe.

    Sections 2.4 and 3.5, pp.4,8 · Main paper

    What to reuse—and what not to infer

    Transferable lesson: Show a counterexample to an attractive metric and report both average and best-of-many performance for ambiguous reconstruction.

    Boundary: Prior plausibility and aligned pose errors do not prove biomechanical or clinical validity. Preserve slash-separated statistic order: Table 5 labels median/mean/std, whereas the prose calls the leading 8.76 value a mean. Mathematical equivalence was scanned, not exhaustively verified.

    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
    5, 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.