Cepheid: Decoupling Scale from Structure for Cross-Family Motion Completion

Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements.

By Su, Ke, LiuOct 13, 2026

Score█████░░░░░4.8

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

Read the original PDF Code

Abstract

Sparse-to-dense motion completion recovers dense mesh motion from sparse vertex displacements. Residual priors trained on one geometry family frequently fail to beat parameter-free harmonic extension off-family. Moreover, cross-family training meets a scaling barrier: residual amplitude varies by more than an order of magnitude across geometry families and collapses as observations become denser. Since this scale depends on unknown target motion, it cannot be measured directly at test time. We decouple residual structure from physical scale by learning a dimensionless prior, estimating amplitude from observables and a lightweight correction fitted only on training sources. Our unified model surpasses harmonic extension across all evaluated families, including unseen cloth and legged bodies, without skeletons, registration, or cross-mesh correspondence. The same prior applies without retraining to point-tracked animal video and a robot grasping a soft body. Project page: https://su-terry.github.io/cepheid-website/

Chih-Hai Su, Tsung-Wei Ke, Yu-Lun Liu

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 24% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 18% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 14% of this desk’s rubric
Novelty2 / 5: A new combination of known ideas.; 16% of this desk’s rubric
Trajectory3 / 5: A clear path to scale.; 18% of this desk’s rubric
Stakes2 / 5: Benefits a professional community (practitioners, clinicians, engineers).; 10% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░ 3	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░ 3	18%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░ 3	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░ 2	16%	A new combination of known ideas.
Trajectory	███░░ 3	18%	A clear path to scale.
Stakes	██░░░ 2	10%	Benefits a professional community (practitioners, clinicians, engineers).

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: gains (orders of magnitude, outperforms); verification (held-out test); scale (scalable, orders of magnitude).

How the score was computed

rank-2026-10-18

Score█████░░░░░4.8

Score = 10 × (65% × adjusted merit / 10 + 25% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.5 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.6 / 10; Shrunk toward the desk prior by editor confidence (40%).
Attention: 64%; Citations, upvotes, points, mentions.
Freshness: 26%; Half-life decay since publication.

Citations0 (reference 15, via semantic-scholar, Oct 17, 2026, 01:35 UTC)
Influential citations0 (reference 3, via semantic-scholar, Oct 17, 2026, 01:35 UTC)
Hugging Face upvotes30 (reference 25, via hf-daily, Oct 16, 2026, 13:29 UTC)
GitHub stars7 (reference 250, via hf-daily, Oct 16, 2026, 13:29 UTC)

The record

Reviewed by heuristic-v7 on Oct 18, 2026, 13:49 UTC. Paper type: empirical.
Categories: cs.CV, cs.GR, cs.LG
BRIEF, No.1 in the Artificial Intelligence edition of October 18, 2026.