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arxiv: 2605.26149 · v1 · pith:DSBKN2GOnew · submitted 2026-05-23 · 💻 cs.GR · cs.CV

AnySurf: Any Surface Generation with Directed Edge

Wenda Shi , Chenyuan Pan , Dengming Zhang , Yiren Song , Biao Zhang , Xingxing Zou This is my paper

Pith reviewed 2026-06-30 12:35 UTC · model grok-4.3

classification 💻 cs.GR cs.CV
keywords 3D surface generationopen surfacesdirected edgesmesh generationgarment modelingface orientationhybrid surfaces3D generation framework
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The pith

AnySurf uses directed edges on a flexible grid to generate open, closed, and hybrid 3D surfaces with correct face orientations.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper claims that previous 3D generators either force watertight closed meshes or produce normal flips and topology mistakes when asked to create open surfaces such as garments. AnySurf replaces the standard grid with a directed-edge version of the Flexible Dual Grid that stores orientation on each edge. A post-training stage called ROS-FT together with a one-percent-parameter adapter then teaches the model to respect those directions. The authors also release an industrial dataset of garments and accessories to train and test the approach. If the method works, a single generator can handle the full range of real-world surface types without domain-specific sewing patterns or post-processing fixes.

Core claim

AnySurf is a unified framework that generates open, closed, and hybrid 3D surfaces with accurate face orientation by building on directed-edge enhanced Flexible Dual Grid (FDG-D), whose oriented grid edges retain normal direction information; ROS-FT post-training and a lightweight DE-Adapter with 1 percent extra parameters enable directed-edge learning while the Outfit3D dataset supports training on industrial garments and closed accessories.

What carries the argument

directed-edge enhanced Flexible Dual Grid (FDG-D), which encodes normal direction on oriented grid edges

If this is right

  • A single generator can produce garments, shoes, accessories, and closed objects without switching representations or sewing patterns.
  • Downstream rendering, simulation, and editing tasks receive meshes that already carry consistent face orientation.
  • The 1-percent DE-Adapter can be added to existing generators without retraining the full model from scratch.
  • Open-surface generation becomes a standard 3D task rather than a specialized garment-modeling pipeline.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same directed-edge idea could be tested on non-manifold or self-intersecting surfaces that current generators avoid.
  • If the adapter preserves performance on closed surfaces, the method may generalize to other grid-based generators beyond the one used here.
  • Accurate open-surface normals would directly improve collision detection and cloth simulation without extra normal-correction steps.

Load-bearing premise

Directed-edge information stored in the grid plus the post-training and tiny adapter is enough to produce correct orientations on open surfaces without creating new topology or normal errors.

What would settle it

Generate open surfaces on the Outfit3D test set and count the fraction of faces whose normals point inward or whose edges fail to form a manifold boundary.

Figures

Figures reproduced from arXiv: 2605.26149 by Biao Zhang, Chenyuan Pan, Dengming Zhang, Wenda Shi, Xingxing Zou, Yiren Song.

Figure 1
Figure 1. Figure 1: Comparison of methods for generating 3D outfits that combine open surfaces (e.g., garments) and closed surfaces (e.g., shoes and accessories). (1) General [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Textureless results of Trellis2 on open surface data (eg, GarmageSet). [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The topological and normal quality with different training steps of Trellis2 on open surface data. SS DiT is the Sparse Structure DiT. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Incorrect face orientation already emerges in the Flexible Dual Grid [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FDG-D (Flexible Dual Grid with Directed Edge) preserves the face [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The (a) training and (b) inference pipeline of our method. A means [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Examples of Outfit3D, GarmageSet, and GarVerseLOD. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison on pure open surface (GarmageSet) generation task. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison on hybrid data (Outfit3D) generation task. [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Qualitative comparison on pure closed surface generation task. [PITH_FULL_IMAGE:figures/full_fig_p014_10.png] view at source ↗
Figure 12
Figure 12. Figure 12: Ablation study of FDG-D on shape VAE reconstruction task. Shape [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Our method is compatible with texturing pipeline of Trellis2. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Multi-item 3D generation with our pipeline can generate open [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
read the original abstract

Open surface components prevail in real industrial 3D content and support rendering, physical simulation and geometric editing. Garments serve as a typical open surface type, with numerous existing generation methods leveraging sewing patterns to generate 2D panels and stitch them into 3D shapes. Such domain-specific designs lack scalability and cannot generalize to shoes and accessories. Common field-based 3D generators prioritize watertight meshes and tend to create flawed double-layer structures on open surfaces. Though Trellis2 adopts field-free representation, its open surface results still contain normal and topology errors. We present AnySurf, a unified framework generating open, closed and hybrid 3D surfaces with accurate face orientation. Built on directed-edge enhanced Flexible Dual Grid (FDG-D), our representation retains normal direction information via oriented grid edges. We also propose ROS-FT post-training and a lightweight DE-Adapter with merely 1% extra parameters, facilitating directed edge learning while preserving original generation performance. We further construct Outfit3D dataset containing industrial garments and closed accessories. Our work transforms garment modeling into a universal 3D generation task. Experimental results demonstrate superior mesh quality and better practicality for downstream applications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The manuscript presents AnySurf, a unified framework for generating open, closed, and hybrid 3D surfaces with accurate face orientation. It builds on a directed-edge enhanced Flexible Dual Grid (FDG-D) representation that retains normal direction information via oriented grid edges, introduces ROS-FT post-training and a lightweight DE-Adapter (1% extra parameters) for directed edge learning, and releases the Outfit3D dataset of industrial garments and closed accessories. The central claim is that this combination produces superior mesh quality without introducing new topology or normal errors, transforming garment modeling into a general 3D generation task.

Significance. If the claims hold with supporting evidence, the work could meaningfully advance 3D surface generation in computer graphics by addressing limitations of watertight-focused field methods and prior open-surface approaches like Trellis2. The lightweight adapter, post-training strategy, and new dataset are potentially useful contributions for practical applications in rendering, simulation, and editing. However, the absence of any quantitative results, ablation studies, error metrics, or implementation details prevents a full assessment of significance or reproducibility.

major comments (1)
  1. The abstract states that 'experimental results demonstrate superior mesh quality' but provides no metrics, tables, figures, or comparisons to support this central claim. Without such evidence, the sufficiency of FDG-D + ROS-FT + DE-Adapter for accurate face orientation on open surfaces cannot be evaluated.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the need for quantitative support of our claims. We address the concern point-by-point below and commit to substantial revisions.

read point-by-point responses

  1. Referee: The abstract states that 'experimental results demonstrate superior mesh quality' but provides no metrics, tables, figures, or comparisons to support this central claim. Without such evidence, the sufficiency of FDG-D + ROS-FT + DE-Adapter for accurate face orientation on open surfaces cannot be evaluated.

    Authors: We agree this is a valid observation for the submitted version. The manuscript text as provided contains only the abstract claim without accompanying quantitative results, ablations, or implementation details. In the revised manuscript we will add a full Experiments section containing: (1) quantitative metrics including normal consistency error, topology error rate, and mesh quality scores on open/closed/hybrid surfaces; (2) direct comparisons against Trellis2 and other baselines with tables and figures; (3) ablation studies isolating FDG-D, ROS-FT, and the DE-Adapter; and (4) implementation details sufficient for reproducibility. These additions will directly substantiate the abstract claim and allow evaluation of the directed-edge components. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The abstract and provided context describe AnySurf as a new unified framework built on a directed-edge enhanced representation (FDG-D) plus two new training components (ROS-FT and the 1%-parameter DE-Adapter). No equations, fitted parameters, or derivation steps are exhibited that reduce any claimed output (accurate face orientation on open surfaces) to an input by construction. No self-citation chains, uniqueness theorems, or ansatzes imported from prior author work are invoked as load-bearing premises. The central claims rest on experimental validation against external benchmarks and a newly constructed dataset rather than on re-derivation of fitted quantities.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 3 invented entities

Based solely on the abstract; FDG-D appears to be an enhancement of a prior Flexible Dual Grid representation while DE-Adapter, ROS-FT, and Outfit3D are introduced here.

axioms (1)
  • domain assumption Oriented grid edges retain normal direction information
    Invoked as the core property of the FDG-D representation.
invented entities (3)
  • DE-Adapter no independent evidence
    purpose: Lightweight module (1% extra parameters) for learning directed edges
    New component proposed to enable directed-edge learning
  • ROS-FT no independent evidence
    purpose: Post-training procedure to facilitate directed edge learning
    New post-training stage introduced in the work
  • Outfit3D no independent evidence
    purpose: Dataset of industrial garments and closed accessories
    New dataset constructed for the paper

pith-pipeline@v0.9.1-grok · 5746 in / 1324 out tokens · 46020 ms · 2026-06-30T12:35:19.426303+00:00 · methodology

discussion (0)

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Reference graph

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