HairDeck: Animation-Ready Stylized Hair Cards from a Single Image

The hair of stylized characters in games and avatars is built from hair cards: artist-placed ribbons whose geometry is the visible hair, animated by spring bones.

By He, Wei, Zhao +3Oct 29, 2026

Score█████░░░░░4.9

Key numbers

33 times faster
44% and card-orientation error by

Caveats

Preprint; not yet peer reviewed.

VerdictWorth a reader's time today.

Read the original PDF Code

Abstract

The hair of stylized characters in games and avatars is built from hair cards: artist-placed ribbons whose geometry is the visible hair, animated by spring bones. Existing image-based methods produce realistic hair or a single fused mesh, while CHARM, the only generator of stylized hair cards, decodes a hairstyle autoregressively as a long sequence of control points. Observing that a card hairstyle is an unordered set of artist-defined primitives, we propose HairDeck, which generates it as one: each card is a single token, and a set diffusion transformer denoises all cards together. From a single image, HairDeck generates the cards and the spring-bone chains that drive them, yielding an animatable asset that exports to ready-to-use downstream formats. On CHARM's own test set, it lowers Chamfer distance by 44% and card-orientation error by more than half while running 33 times faster, and from single images it outperforms CHARM, image-to-3D pipelines and strand-based methods. Project page: https://hyzcluster.github.io/HairDeck

Yuze He, Yixiang Wei, Wang Zhao, Chuhao Chen, Yanning Zhou, Yong-Jin 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
Magnitude4 / 5: A qualitative jump: a capability or regime that did not exist before.; 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
Trajectory2 / 5: Some room to improve with obvious engineering.; 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	███░░3/5	24%	A method or resource many groups across the field will adopt within a year.
Magnitude	████░4/5	18%	A qualitative jump: a capability or regime that did not exist before.
Evidence	███░░3/5	14%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	██░░░2/5	16%	A new combination of known ideas.
Trajectory	██░░░2/5	18%	Some room to improve with obvious engineering.
Stakes	██░░░2/5	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: method (we propose); gains (x-fold, relative gain, outperforms); verification (held-out test); scale (efficient).

How the score was computed

rank-2026-10-18

Score█████░░░░░4.9

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 (42%).
Attention: 43%; Citations, upvotes, points, mentions.
Freshness: 80%; Half-life decay since publication.

Hugging Face upvotes16 (reference 25, via hf-daily, Oct 30, 2026, 13:37 UTC)
GitHub stars0 (reference 250, via hf-daily, Oct 30, 2026, 13:37 UTC)

The record

Reviewed by heuristic-v10 on Oct 30, 2026, 13:36 UTC. Paper type: method.
Categories: cs.CV, cs.GR
TOP, No.6 in the Artificial Intelligence edition of October 30, 2026.