Unsupervised Representation Learning for 3D Mesh Parameterization with Semantic and Visibility Objectives
Authors: AmirHossein Zamani
Bruno Roy
Arianna Rampini
Venue: ICLR 2026 Poster (2026)
Keywords: Unsupervised representation learning
Mesh parameterization
Semantic-aware UV mapping
Visibility-aware UV mapping
Primary area: learning on graphs and other geometries & topologies
TL;DR: We propose a novel unsupervised method for 3D mesh parameterization that integrates semantic- and visibility-aware objectives to eliminate manual UV mapping, accelerate 3D content creation, and improve texture quality.

Abstract: Recent 3D generative models produce high-quality textures for 3D mesh objects. However, they commonly rely on the heavy assumption that input 3D meshes are accompanied by manual mesh parameterization (UV mapping), a manual task that requires both technical precision and artistic judgment. Industry surveys show that this process often accounts for a significant share of asset creation, creating a major bottleneck for 3D content creators. Moreover, existing automatic methods often ignore two perceptually important criteria: (1) semantic awareness (UV charts should align semantically similar 3D parts across shapes) and (2) visibility awareness (cutting seams should lie in regions unlikely to be seen). To overcome these shortcomings and to automate the mesh parameterization process, we present an unsupervised differentiable framework that augments standard geometry-preserving UV learning with semantic- and visibility-aware objectives. For semantic-awareness, our pipeline (i) segments the mesh into semantic 3D parts, (ii) applies an unsupervised learned per-part UV-parameterization backbone, and (iii) aggregates per-part charts into a unified UV atlas. For visibility-awareness, we use ambient occlusion (AO) as an exposure proxy and back-propagate a soft differentiable AO-weighted seam objective to steer cutting seams toward occluded regions. By conducting qualitative and quantitative evaluations against state-of-the-art methods, we show that the proposed method produces UV atlases that better support texture generation and reduce perceptible seam artifacts compared to recent baselines. We will make our implementation code publicly available upon acceptance of the paper.

Published as a conference paper at ICLR 2026
UNsUpERVISED REPRESENTATION LEARNING FOR 3D MESH PARAMETERIZATION WITH SEMANTIC AND VISIBILITY OBJECTIVES
AmirHossein Zamani 1,2,3 1.2.3 3
Bruno Roy"
Arianna Rampini
1 Autodesk Research
Mila Quebec Al Institute
3 Concordia University
Project page: https://ahhh22991 97w. 10/ Automat a zation/
Semantic-Aware Parameterization
Visibility-Aware Parameterization
Semeate Almc
Yaukdity Seoce Haah
Figure mesh parameterizations generated by our semantic-aware (left) visibility-aware (right) pipelines. Semantic-Aware (left, Sec. 3.3): To encourage semantically coherent UV charts for easier tex- ture editing, given input mesh (a), we design partition-and parammterie strategy, (b) compute per-vertex semantic partition, (c) Iearn geometry geoseett--reseevinng UV parameterization (Sec. (3.2) independently for each part obtain UV islands, and finally aggregate pack these islands into unified UV atlas (insets). Visibility- Aware (right, Sec.(3.4): To encourage seamless UV mappings, the pipeline (d) takes an input 3D mesh, jointly (c) guides cutting-scam placement (red curves), extracts corresponding UV boundary points (red dots), and (f) estimates global ceometry-preservie parameterization. This steers cutting seams toward less-visible (more accluded) surface regions, producing more visually seamless UV maps.
ABSTRACT
Recent 3D generative models produce high-quality textures for 3D mesh objects. However, they commonly rely on the heavy assumption that input 3D meshes are accompanied by manual mesh parameterization (UV mapping), manual task that requires both technical precision and artistic judgment. Industry surveys show that this process often accounts for a significant share of asset creation, creat- ing a major bottleneck for 3D content creators. Moreover, existing automatic methods often ignore two perceptually important criteria: (1) semantic aware- ness (UV charts should align semantically similar 3D parts across shapes) and (2) visibility awareness (cutting seams should lie in regions unlikely to be seen). To overcome these shortcomings and to automate the mesh parameterization pro- cess, we present an unsupervised differentiable framework that augments stan- dard geometry- geometty-preserringg preserving UV learning with semantic- and visibility-awar objec- tives. For semantic-awareness, our pipeline (i) segments the mesh into semantic 3D parts, (ii) applies unsupervised learned per-part parameterrizziionn back- bone, and (iii) aggregates per- part charts into a unified UV atlas. For visibility- awareness,
ambient occlusion (AO) as exposure proxy and back- propagate a soft differentiable AO-weighted seam objective to steer cutting seams toward occluded regions. By conducting qualitative and quantitative evaluations against state-of-the-art methods, we show that the proposed method produces UV atlases that better support texture generation and reduce perceptible seam artifacts compared to recent baselines. Our implementation code is publicly available at: http://ggitul.oomm/HHHz277/SSSS Disiomanttiiiiiibltttt-UUU Visibility-UV-Parann
"Correspondence rhosse zamani @mi queueecc

---

Published as a conference paper at ICLR 2026
INTRODUCTION
Texture mapping, also called UV/Mesh parameterization, converts a 3D mesh surface into a 2D im- age and is core step in digital 3D content production, enabling texture synthesis, painting, transfer, and high-quality rendering. While recent 3D generative methods produce high-quality textures for 3D mesh objects, they commonly rely on the heavy assumption that input 3D meshes are accompa- nied by the mesh parameterization process that requires technical precision and artistic judgment. This assumption limits usability for content creators and designers. Moreover, industry surveys show that this process often accounts for a significant share of asset creation, creating a major bot- tleneck for 3D content creators. To address these issues, numerous methods have been proposed that mainly focus on geometrie properties such as bijectivity (Tutte) 1963,, Shhffeer et al. (007a), conformality sungle-preservation) (Lévy et al.] 2023). ) stretch (length- preggth-resrrvttionn (Sander et al.) 2002), and equiareality preseerpesrrvtiooo)) (Zou et al. 2011). While these properties are necessary to achieve a high-qualiny parameterization, they are not sufficient for many downstream applications in content creation and texture synthesis. In particular, two percepiual criteria are often neglected: (I) semantic awareness, 2D UV charts should align with semantically meaningful surface 3D parts so that textures designed for semantic region remain coherent and transferable across 3D shapes; and (2) visibility awareness. seams should be placed where they are unlikely to be observed un- der typical viewpoints and lighting, so that seam artifacts less perceptible after texturing and rendering. To the best of our knowledge, only limited work has explored these properties individu- ally: semantic-aware UV mapping (Vermandere al., 2024) Mukherjee et al., (024) and visibility- aware UV learning (Weill-Duflos et al.] [2023] Sheffeer & Hart [002] Ray et al.) 2010). However, none of the existing approaches address both objectives simultaneously. To overcome these short- comings, we present an unsupervised differentiable framework that augments standard geometry- preserving UV learning with semantic- and visibility aware objectives. For semantic-awareness, our pipeline (i) segments the mesh into semantic 3D parts, (ii) applies an unsupervised learned per- part Ua-ppaaamtterizattion backbone, and (iii) aggregates per-part charts into unified UV atlas. For visibility- we use ambient occlusion (AO) (Zhukov et al.) 1998) as an exposure proxy and hack-propagate a soft differentiable AO-weighted seam objective designed to steer cutting seams to- ward occluded regions. The proposed pipeline has two stages: (i) a neural murface-parameteriinn backbone enforces gcometry-preservie ules provide task-specific (e.g., texture painting) guidance. The main contributions of this study are: (i) We propose an unsupervised. differentiable framework that jointly estimates geometry- prreeerr-p UV parameterizations while optimizing semantic- and risibility-aware objectives. (ii) We introduce two novel perceptual objectives that directly connect UV parameterization to texture synthesis: a semantic-aware objective that aligns 2D UV charts with semantically meaningful 3D surface parts, and visibility-awwe objective that places seams in less-visible regions to minimize seam artifacts.
properties, (ii) semantic- awarre and visibility-awan mod-
RELATED WORK
Traditional UV Parameterization. These methods aim to flatten 3D surfaces to 2D while min- imizing distortion and avoiding overlaps. Early conformal (angle-preserving) techniques such as LSCM (Lévy et al. 2023) and ABF++ (Sheffer et al., 2005) reduce angular distortion via global optimization or iterative angle adjustment. Many approaches also enforce bijectivity and bound parameterization (Sorkine et al. 2002) limits local stretch by cut- ting into patches, bijective bree-boundary methods (Smith & Schaefer 2013) avoid overlaps without fixing boundaries, and SLIM (Rabinovich et al. (017) scales these guarantees to large meshes. Op- timizing cuts and chart segmentation also have been addressed: AutoCuts (Poranne et al. 2017) and OptCuts (Li et al. 2018) jointly optimize seams and UV parameterization to trade off distortion and chart count, while Seamster (Sheffer & Hart (2022) and Seamless (Liu et al., 2017) place seams to minimize visible texture artifacts by aligning them with features or low-visibility regions. Although classical algorithms provide the geometric foundation for UV mapping, they often treat these goals independently and rarely exploit higher-level objectives (e.g., semantic parts or visibility), which can limit final texture quality.
distortion:
Learning-Basse UV Parameterization. The existing methods for UV parameterization can be grouped by input representation: explicit (meshes and point clouds) (Zhao et al. 2025 Zhang al.)
2024b] Srinivasan et al. 22024; Chen et al. 2022) Teimury et al. 2020; Oechsle et al.
2019: Knodt
et al. 2023), implicit (NeRFs) (Xu et al.] 2024a Xiang et al.] 2021 Srinivasan et al.
2024 Guan 2024b). In
jet al., 2023; Das et al. 2022). and 3D-Gaussian -slassiin----tttgn----a based methods (Xu et al.
this work, focus on explicit (mesh) parameterization, which is widely used in industry. Mesh-
2

---

Published as a conference paper at ICLR 2026
Step Semantic 3D Partitioning
Step 2: Geometry Preserving UV Learning
Step 3: UV Atlas Aggregation & Packing
Segnrmmoo Purts
UV Cocrdieates foe Taris Segreened in Part
Fiaal UV Coandaazia
Segranated Meal
Inget Mesk
Enteaee Hoed
Povm tng Leue
Guunstyy
bete Modil
Pusor lag
Figure 2:
An overview of the training of the proposed semantic-awaar UV parameterization method (Sec. 3.3 consisting of three stages: (i) semantic 3D partitioning, computes a per-vertex semantic partition of the input mesh using shape diameter function (Appendix A.T]; (ii) geometry-preservin
UV learning, applics the base UV-parameterizatioon backbone (Sec. [3.2] independently to each semantic part to obtain per-part UV islands; and (iii) UV atlas aggregation and packing, aggregatts and packs these islands into a unified UV atlas.
based learning approaches (Zhao et al.
2025 Srinivasan et al. 2024 Zhang et al. 2024a b) usually
train multi-layer perceptrons (MLPs) to learn UV mappings by optimizing three core criteria: (i) minimizing chart count, (ii) reducing distortion (preserving lengths, angles, and areas), and (iii) enforeing bijectivity via round-trip consistency. These MLPs are trained with carefully designed losses to satisfy those constraints. However, current learning-basse based methods often (i) lack semantic awareness, meaning that cutting seams are chosen only to reduce geometric distortion rather than align with meaningful parts, and (ii) ignore visibility when placing seams, failing to steer cuts toward low-visibility regions and thus producing noticeable texture artifacts.
METHODOL.OGY
We propose a two-stage, unsupervised framework to automatically learn 3D mesh parameterizations with semantic and visibility objectives. The training consists of two stages. In stage (i), geometry- preservation mesh saahh-arramtteizatioon learning (Sec. (S.c.3.2). an ML.P-based network trained with fully differentiable geometric objectives defined in the UV domain to produce initial UV mapping that satisfies low-distortion requirements. In stage (ii), learning perceptual objectives (Sec. 3.3 and Sec. 3.4.. we introduce two perceptual objectives: semantic awareness and visibility awareness. By semantic awareness, we mean that the UV charts produced by stage (i) should align with seman- tically meaningful surface 3D parts. To this end, adopt partition-ande iaitttionnnd-ppraaaae aadd-paameetrrie strategy based on the Shape Diameter Function (Shapira et al. 2008): we first partition the input mesh into semantic 3D parts, learn UV parameterization for each segmented part with the same MLP-based architecture used in stage (i), and finally aggregate the per-part UV charts into a single unified UV atlas. By visibility awareness, we mean that cutting seams should be placed on 3D mesh surface regions that are unlikely to be seen under typical viewpoints and lighting, making seam artifacts less perceptible after texturing. To this end, we propose a visibility occlusion as a proxy for viewer exposure (Zhukov et al.) 1998), encouraging seam placement in re- gions of low exposure. An overview the training procedure for semantic awareness and visibility awareness pipelines is shown in Fig. and Algorithm respectively.
objective that uses ambient
3.1 PROBLEM STATEMENT AND PRELIMINARIES
Given a 3D mesh M = (V, F) with vertex positions V E N.33 (where No is the number of vertices), triangular faces F' E IR xx3 (where N₂ is the number of faces), and per-vertex normals N IR- x. our goal is learn pointwise UV parameterization up V + R2 that maps each ver- tex in V to a 2D coordinate, producing UV coordinates Q E N with row-wise correspondence between V and Q. We require the learned parameterization to satisfy the following properties during the learning process: (i) geometry preservation, i.c., the parameterization should be low- www-iittotion and ideally preserve classical geometric properties such as (a) isometry (maintaining original edge lengths), (b) equiareal (preserving triangle/facet areas), and (c) conformality (preserving angles be- tween edges); (ii) UV-space efficiency and bijectivity, the mapping should (a) efficiently utilize the available UV domain, (b) avoid overlapping projections of distinct 3D points, and (c) be one-to- one so that each surface point maps to a unique UV location; (iii) high-quality cutting seams, the parameterization should place seams in visually inconspicuous regions (Weill-Duflos et al.) 2023 Ray et al., 2010: Sheffeer & Hart) 2002); and (iv) semantic awareness, the UV mapping should se- mantically align 3D parts across different 3D) mesh models to consistent regions in UV space to
3

---

Published as a conference paper at ICLR 2026
enable cross-shape correspondence /Vermandere al.
2024 Mukherjee et al. 2024 Cohen-
et al. 2025).
3.2 BASE NEURAL ARCHITECTURE FOR UV-PARAMETERIZZATON LEARNING
To emulate the sequence of operations an artist would typically perform when constructing a UV atlas for 3D mesh, such as stretching a canvas, placing it over the 3D surface, adding surface cuts where needed, and finally flattening it back to the plane, we build upon the idea of a deforming a 2D grid (Yang et al. 2018) by leveraging bi-directional cycle mapping backbone, proposed in (Zhang et al., 2024a by Srinivasan et al. 2024 Zhao et al., 2025). We emphasize that this stage adopts an existing previously proposed oi--istortionn parameterization backbone. We do not claim novelty for the backbone itself. Instead, our primary contribution lies in the design and integration semantic-aware and visibility-awanr objectives as downstream tasks built on top of this established geometric foundation. Intuitively, the bi-directional cycle mapping enforces that projecting a 3D surface patch into 2D UV coordinates and re-projecting those UVs back onto the 3D mesh yields the original data. Therefore, during the learning process, any seams, distortions, or semantic mis- alignments show up as reconstruction errors. Then, by minimizing these errors, the model is guided to produce an approximately invertible and coherent parameterization that minimizes distortion. To build such a mapping, inspired by recent advances in global parameterization networks (Zhao et al.) 2025 Srinivasan et al. 2024). our backbone is composed of four geometrically interpretable sub- networks: (i) deforming network, which starts from a regular 2D grid and adaptively deforms it into candidate UV coordinates; (ū) wrapping network, which maps those 2D UV candidates to the 3D) surface by smoothly bending the deformed grid to conform to the input mesh, bridging the 2D and 3D domains; (iii) cutting network, which operates on the wrapped 3D surface to open the closed geometry by placing seams that reduce distortion for subsequent flattening; and (iv) unwrapping nenwork, which projects the resulting open 3D surface back to 2D while preserving smoothness and geometric coherence. In this design, Each subnetwork is a pointwise MLP applied to mesh vertices, and the whole architecture is trained end-to-end in an unsupervised fashion. The four subnetworks form two complementary branches enforcing 2D-3D-2D and 3D-2D-3D cycles for ensuring con- sistency across 2D and 3D domains. Coupled with differentiable losses that encourage bijectivity and minimize distortion, this backbone produces candidate UV parameterizations for the next stages. Further details on the loss design and the interaction of the four sub-networks within the two-branch bi-directional cycle are given in Appendix A.2
3.3 SEMANTIC-AWARY UV PARAMETERIZATION
The UV parameterization, obtained from the bi-directional cycle mapping, is used in the next step, semantic- UV parameterization. To encourage semantically coherent UV charts that simplify texture editing, transfer, and cross-object correspondence, we design a partition-ane crriitionannd-peee and--aaameetriiz pipeline (illustrated in Fig. 2. Our semantic-aware parameterization has three stages: (i) compute per-vertex semantic partition of the input mesh using the shape diameter function (ShDF) (Shapira) al. 2008); (ii) apply the base uV-paramettrrization backbone from Sec. 3.2 independentlly each semantic 3D part to obtain per-part UV islands; and (iii) aggregate and pack these islands into a unified UV atlas. We now describe each stage in detail.
3D Partitioning using Shape Diameter Function. Given an input mesh M = (V, we com- pute semantic partition S V + {I...., K} using the shape diameter function (ShDF) (Shapira et al. 2008) as our primary signal. Intuitively, the ShDF maps each surface sample to a scalar that estimates the local object thickness (the diameter of the object in the vicinity of that sample). Sim- ilar ShDF values typically indicate coherent semantic parts (e.g., limbs, body, handles), making the ShDF practical cue for part-level partitioning of 3D shapes. Specifically, our 3D partitioning strat- egy consists of the following steps (See Appendix. A for the detailed explanation of each step): (i) compute local ShDF, we compute local thickness (ShDF) per face via ray casting inside a cone about the inward normal at each surface point and smoothing the samples for spatial coherence; (ii) fu GMM, to obtain soft fer-face class likelihoods, we use 1-D Gaussian mixture model (GMM) to fit K (the deisred number of semantic components) Gaussians to the fer-face thicknesses; (iii) boundary smoothness cost, to bias the partition toward spatially coherent regions, we then combine these likelihoods with geometry-aware pairwise smoothness penalty (e.g.. a dihedral-angle based boundary cost) on adjacent faces on the geometry to encourage coherent regions and penalize label changes across sharp edges. (iv) initial labeling, we then assign each face to the GMM component with maximum probability (minimum negative likeliiii.oood). This produces a purely data-driven segmentation that is generally noisy but captures the major mode structure of the thickness field.
4

---

Published as a conference paper at ICLR 2026
(v) refine with graph cuts, then refine the initial assignment by minimizing global energy (see Eq. 10 in Appendix A.II via iterative appha-expansion. solving each expansion by an optimal min- cut. Repeating this process across labels and iterating few times yields stable and low-energy 3D partitions. (vi) Relabel connected components, after refinement, a single label index can correspond to multiple disconnected components. To solve this issue, we relahel each connected component of faces so that every final label corresponds to single connected sub-mesh. This guarantees that sub- sequent per-part processing operates on contiguous regions. (vii) Post-processing, to produce clean 3D segmentation results, we apply several simple post-processing heuristics to remove spurious tiny components that would complicate per-part UV parameterization. The output then a set of con- nected semantic sub-meshes {Ma = (Vε, F=) suitable for the per-part UV parameterization stage described in Sec This high-level pipeline is simple, robust, and fast. Full algorithmic and implementation details are provided in Appendix ArpeeniixA..fee A.II........ for reproducibility. We also evaluate a modern zero-shot segmentation baseline, SAMesh (Tang et al. 2024), as an alternative partitioner and report comparative results in our ablation study.
Per-part Parameterization. For each semantic submesh MM. obtained from previous step, we instantiate the base parameterization backbone, described in Sec. 3.2. and Appendix A.2 and op- timize a part-specific mapping uA, Ve + IR² Each part is trained with the same suite of dif- ferentiable objectives used for the global model. Therefore, minimize the per-part loss
Algorithm Visibility- Awwre UV Learning
Require: Mesh M (V, F) with edges E, vertices V e R No normals E &NNe faces F é 2 per- vertex AO E 2= iiterations T', and model weights to
1:
2:
SLep 1: Learning UV map
Q - UV Backbone P) forward pass
4:
On normalizazion(((
Norm.
5:
Swrap + Chamter(P, P. wrар Bg.
6
Cpar (0x) = C(())
wrap + c.(() + repel + dist
c())
((k)
(I)
6:
Travas (ep(Q..)
Rep.
loss 7q.
7:
Cycle-consiseency loss (Eq
17
where ((k) E < and (k)
dist de-
Coyeleg := 11- @eyciall 112 + ||p P1l?
note the wrapping objective (Eq. 16]. cycle-
consistency regularizer (Eq. [7]. anti-overlap
Loga CosSim P'.., Pon)
penalty (Eq. 18 and distortion penalty (Eq. 19 respectively (see Appendix AA. for definitions); each term is evaluated on the submesh Ma. To minimize Eq. [] we compute the gradient Vol Cfr (0x) for the bi-directional cycle map- ping subnetwork of part and apply a gradient- (A) descent update using 02 + Og 1) Vo, Vo, -.rss (0%) where 7j is the learning rate. This yields K in- dependently trained bi-directional sub-networks (one per semantic 3D part produced by our ShDF segmentation). The resulting per-part parameter- izations are then passed to the aggregation-ans packing stage to form a unified UV atlas. Train- ing parts independently allows each semantic re- gion to adopt chart that best trades off local ge- ometric fidelity and developability.
8: 61,52 Compute Differential. Properties Pe.. Q1
Lqja f- Loo (c1_ca) ce) ) LIDO. (Q. V. 1*) Egg
10:
Set hase loss (Fq. 15
Law Lrmp + 0.01 Lumarp 0.01 Les +
0.005 Luc 0.01 Lini + 10 Logant
11: Scep 2: Opt imizing Sur face Cuta Differentiable neighbor selection (Eq (:
12:
d: + log( (E, Eycx exp(7 l1 - 9. 2l))
13:
Differentiable seam extraction (Fq
#, + o((() -5
14:
Compute values (Eg.
4:
AO(D) 1 Sa (w) VP,.w) (m(p) dw
15:
S + Dvev 5; +
16:
Lan + Σiev AO,
# seam loss (Egg visibility Joss (1g.
17:
Luu Lrow + 0.001 Lio
18:
(0) 8 t B n1
Atlas Aggregation and Packing, After opti- mizing all parts, we obtain per-part UV islands {Qx [0. 1| (each normalized to the unit square) corrsponding to each 3D segmented part.
19:
20:
end
21:
return
These islands are merged into a single atlas by an atlas aggregator. For clarity and reproducibility our current aggregator is intentionally simple and deterministic: the final UV sheet is unit square subdivided into G x G regular grid, where G = [VK] We then assign each part k unique grid cell indexed by row and column (Tk, cx) in row-major order. Each normalized island Qs is placed into its assigned cell via a uniform similarity transform (scale s and translation tra.co
2 pad
c + pad
r +-dd"
Tu( = 8 4
Lec =
(2)
G
G
G
where pad E [o, 0.5) is a small margin (in normalized UV units) to prevent bleeding between neighboring cells. Using the same scale s for all charts enforces consistent texel density across
5

---

Published as a conference paper at ICLR 2026
parts, which simplifies texture baking and editing workflows. The final per-vertex UV for the whole mesh is therefore
Ufinal (v) Ti(uo, (v)), V.
(3)
The grid- rrid-based aggregator is simple, deterministic, and effective for per-part editing and texture transfer, and can be replaced by more advanced packing solvers (heuristic or optimization-basel without changing the per-part losses in Eq. [ Overall, the proposed partition-and-patt paaa--aaddd-praeeiiiiiie de- sign preserves the geometry-focusee objectives of Sec. S.2. while producing semantically organized UV atlases that are easier to edit and use for correspondence.
3.4 VISIBILITY-AWARE UV PARAMETERIZATION
A common perceptual failure of UV atlases is that seams positioned exposed, well- lit, or frequently viewed regions create visible discontinuities once textures are applied. To address this, introduce visibility-aware objec- tive that biases seam placement toward surface areas with low viewer exposure. As proxy for visibility, we leverage ambient occlusion (AO) (Zhukov et al. 1998). a standard geometric measure how much of the local hemisphere around surface point is blocked by nearby geometry (Landis] [0002] Christensen 2002
side Vew
Freet View
Tap SediYion
Bavoil & Sainz 2008; McGuire. 2010 Laine & Kar- ras 2010 Méndez-Feliu & Sbert 2009). Our training
May Paweaaa maeee dimeett
procedure consists of four steps: (1) compute per-vertex
AO values on the input mesh, (ii) apply the base UV-
parameterization backbone (Sec. (3.2) to obtain candi- date UV islands, (iii) extract UV boundary points cor- responding to cutting seams, and (iv) compute and min- imize the AO-weighted average over the associated 3D vertices. This objective guides the backbone to produce UV islands whose seams coincide with regions of low exposure, thereby relocating cuts to less visible areas of the 3D surface and reducing perceptible seam artifacts after texturing and rendering. We now detail each step in turn.
Figure 3: Qualitative results of the proposed semantic-aware UV parameterization method Rabbit mesh. For each method, we show the rendered 3D object from multiple viewpoints. with the corresponding UV at- las in the rightmost column. As shown, our method produces UV charts that align more semantically with the mesh's 3D se- mantic parts, unlike the baselines.
Compute Per-Vertex Ambient Occlu- sion. Let be a point on the sur- Define
face with unit normal n(p). the hemisphere of outgoing directions as ²*
(w E $2 n(p) w >> 0}.
The binary visibility function V(p.w)
[1, the ray starting at in direction does not intersect the surface (unoccluded)
(0, otherwise (occluded)
measures
whether direction is visible from p. The ambient-occlusion exposure AO(p) E [0,1] then defined as the cosine-weighted average visibility over the hemisphere 22* (p):
π AO = 1 / (2) V(p, w) (n(p) dw.
(4)
By this convention, A0(p) = indicates a fully exposed point (no occlusion) and AO(p) = 0
indicates fully occluded point. practice AO estimated by Monte Carlo hemisphere sampling
[Veach] 1998) or by high-quality offline ray sampling (Pharr et al. 2023) for evaluation. In our im- plementation, we compute ambient occlusion using the libigl geometry processing library (Jacobson
al. 2018).
Mesh Parameterization. Once ver-eertex AO values are com- puted, we generate the UV parameterization using the base backbone described in Sec. 3.2 and Appendix. A.2 to obtain a potential UV parameterization candidate.
Learning Choomety-Prreerviine
Cutting Seam Extraction in UV Space. We then extract cutting seams directly from the UV mapping learned in the previous step. This process has three steps: (i) neighbor selection, given a set of 3D surface points P with their corresponding UV coordinates Q, the method determines whether each vertex lies seam by exploring its neighborhood. For each 3D point p: E P

---

Published as a conference paper at ICLR 2026
with UV coordinate q, Q, we first find its N nearest neighbors in 3D, denoted {pis) with corresponding UV coordinates {æ.a} (ii) differentiable max UV distance computation, we then (similar to Zhao et al. (2025) compute the maximum UV distance between qi and its neighbors:
Tji max (1ly: 0..1l2)
(5)
Next, we define a threshold r based on the UV domain size. Specifically, we compute the bounding square of the UV map with side length I(Q), and set 7 Tscale [(Q) where Tscale is constant (e.g., 0.1 by default). The intuition behind Eq. S is that, during training, at each iteration we must identify the cutting-seam (boundary) points in UV space in order to evaluate their ambient occlu- sion values and then compute the visibility-aware loss in Eq. 7 Minimizing this loss encourages the model to learn UV parameteriizatinn whose seams lie in less-visible (low-AO) regions. Eq. [5] provides a mechanism for detecting these seam points. Rather than locating seams directly on the 3D mesh, we detect them in the UV domain based on how local neighborhoods deform during un- wrapping. Specifically, for each 3D vertex we know both its 3D coordinates and its corresponding UV coordinates at the current iteration. Therefore, we examine the 3D neighbors of each vertex and compare their mapped UV positions. If pair of vertices are close neighbors on the 3D sur- face but become separated by more than a threshold distance in UV space, this indicates that the surface has been cut between them. We therefore mark such UV points as seam (boundary) points. This criterion is grounded in the fact that any valid unwrapping must introduce discontinuities (i.e.. seams): vertices that are adjacent on the 3D surface become far apart in the UV plane after the mesh is cut open. Eq. formalizes this idea by detecting precisely those locations where this large separation occurs. However, this maximization problem in Eq. [5] is not inherently differentiable, so we introduce mathematical modifications to ensure differentiaaility during the learning process. Specifically, instead of a hard decision, we assign each vertex differentiable soft seam membership score: 8; o (3 (7)- - -)) where a-)) is the sigmoid function and / controls the sharpness of the transition. The maximization problem can then be formulated as a differentiable function:
1/1 ~ log
Σ JENZ
exp(1 lla: 9..5 (1,)
8; = -BBBB -T)) +)),
T = Tscale L(Q)
(6)
7
where NGN denotes the N nearest 3D neighbors in the 1 l-ring of vertex i, and B, Tscale control the soft-max/ sigmoid sharpness and threshold scale. With this formulation, vertices with s; close to 1 are highly likely to lie on seams, while vertices with s; near 0 are not. This procedure produces a soft mask E [0, 1] that marks the cutting seams in UV space and used in the next step to compute the ambient occlusion loss on the seam vertices.
-wwwee Seam Loss. After identifying soft seam memberships 8 E [Ó. 1] for each vertex 8% we en- courage seams to lic in less visible regions of the surface. As visibil- ity proxy, we use per-vertex ambient occlusion values AO; E [0, 1]. com- puted in the first step, where 0 indi- cates fully occluded and 1 indicates fully visible. The loss is defined as the weighted average of occlusion values, using soft seam mem- berships as weights:
Table I: Quantitative comparison of the proposed visihility-aware UV parameterization method against baselines on multiple evalua- tion metrics.
Method
Visibilly.
Conformality
Equiarcality
Time
OpeCinns al
0 28555
6.931
0,894
240
FeesPura Tangue C Ciart Zhaa et 2025
0.8604
0.9097
0.6759
2
Ours- Visihilty-Aoom Juare Param
0.6068 0.654
0.9175
0,6033
2
Ounx Semmnnic- Visihiley-huorr Parim
0.9153
0,6399
15
CAO = Σ, s; AO;
Σ. +
where E is small constant to avoid division by zero Intuitively, if the network assigns high seam weight s; to highly visible vertices (high AO,), the loss is large. Conversely, assigning seam weight to occluded vertices (low AO;) reduces the loss. Therefore, minimizing Lso guides seam placement toward regions of low exposure.
Integration with The Backbone Losses. To jointly optimize geometric and visibility properties, we augment the visibility- seam loss LAO with the backbone objective from Sec. 3.2 and Appendix. A.2 The full visibility-awart objective is then becomes
(8) where Lwrap: Leyele, Crepel. and Laist denote the wrapping objective (Eq. [6], cycle-consistencyy regularizer (Eq, 17), avti-overlap penalty (Eq. [8), and distortion penalty (Eq. [19). respectively
(f) = Lurap + Leyele + Crepel + Laist + Avis LAO
(see Appendix A.2 for definitions). To minimize Eq.. 8, we compute the gradient V Lvs (0) for
7

---

Published as a conference paper at ICLR 2026
Side View Pront View Back View Button View
Side View Frunt View Bark View Betton View
Side View
Froed View Back View Botton View
Ambiant
Chire
Planshira
OptCann
Figure 4: Qualitative results for visibility aware seam placement and UV parameterization on three repre- sentative meshes. For each mesh, the top row shows per-vertex ambient occlusion (yellow exposed, purple occluded). Beneath are the visualizations of cutting seams (red) from method, FlexPara, and OptCuts (top bottom). Our method places larger fraction of seam geometry in less-exposed regions, reducing the likelihond of visible seam artifacts under typical viewpoints.
the bi-directional cycle mapping network, and then apply gradient-descent update using 0 7) VoLvis (f) where r is the learning rate.
Rabbit
Fish
Duckie
4 EXPERIMENTS AND RESULTS
Ours
We evaluate our unsupervised semantic- and visibility-awarr UV parameterization pipelines a diverse collection of meshes and compare against several baselines. We provide both qualitative and quantitative comparisons to: FlexPara (Zhao et al. 2025) (recent state-of-the-art global parameterization network), Autodesk Maya (Autodesk, Inc., 2025 (industry-grade commercial software), Blender (Blender Foundation] (2055] (popular open-source modeling tool), and xatlas (jpcy) (025)) (widely-used C++ C++ library for generating unique texture coordinates suitable for lightmap baking and texture painting).
FlexPara
OptCuts
Figs. [ 3 and show representative qualitative results, and Tables. I and 3 presents quantitative results of our semantic-aware and visibility-wware pipelines against baselines (additional results are provided in Appendix (A.3). In the following, we detail each experiment and its corresponding results.
Figure 5: Checkerboard texturing using UV maps produced by visibility-aware method, FlexPara, OptCuts. Each row shows ren- dered views of different meshes textured with a checkerboard and a magnified inset of a visually important region cause method seams (red circles). Be- toward occluded regions, checkerboard pattern appears sub- stantially more continuous from typical camera viewpoints. By contrast, baselines exhibit visi- ble seam artifacts in the zoomed-in insets.
4.1 QUALITATIVE EVALUATION
Comparative Evaluation of Semantic-Aware UV Parameterization. To evaluate the performance of the proposed semantic-aware UV parameterization pipeline, we compare our method against FlexPara, Autodesk Maya's Automatic UV generator, Blender's 's SmanUV, and xatlas. Fig- 3 shows a representative comparison on single Bunny mesh; additional results more diverse and complex meshes are provided in Figs. [ and 12 in Appendix AA.3 In Fig. 3 and Fig. []] each row corresponds one method. Semantic parts are color-coded, and columns show the rendered 3D mesh from multiple camera viewpoints. The rightmost column depicts the unwrapped UV coordinates, where UV is- lands are color-coded consistently with their corresponding semantic 3D parts in the rendered views. As shown, our method successfully identifies semantic parts of the input mesh, parameterizes them, and maps them into UV space with low distortion. This leads to cleaner,
interpretable charts
8

---

Published as a conference paper at ICLR 2026
that directly benefit downstream tasks such texture painting and transfer. By contrast, the baseline methods fail to consistently capture and parameterize semantic parts of the 3D mesh.
Comparative Evaluation of Visibility-Aware UV Parameterization. To evaluate the performance of the proposed visibility-aware UV parameterization pipeline, we compare our method against FlexPara (Zhao et al. 2025) and OptCuts (Li et al. 2018). Figs, and Elpresent representative com- parisons on different meshes. Additional examples appear in Figs. 5 [9 and TO in Appendix [A.3 In Fig. the top row visualizes per-vertex ambient occlusion (AO): lighter tones (e.g., yellow) in- dicate high exposure (more visible) while darker tones (e.g., purple) indicate low exposure (more occluded). A desirable parameterization therefore places cutting seams on darker, less-visible re- gions to reduce perceptible seam artifacts after texturing and rendering. The rows beneath show UV parameterizations produced by our method, FlexPara, and OptCuts (in that order). Qualita- tively, our method allocates a larger fraction of seam geometry to low-exposure regions compared to the baselines. Table I (and Fig. in Appendix A.3) quantitatively supports this observation: the report the mean AO values of seam vertices for each method, where lower scores indicate seams lo- cated in less-visible (more occluded) areas. As shown, our method consistently attains lower mean seam-AO, confirming that the visibility-awe -aware objective successfully biases seam placement toward less-exposed surface regions. Also, Fig. [ illustrates checkerboard texturing on different mesh ob- jects using parameterizations from our method and from baselines. As shown, our method produces noticeably more scamless texture patterns when rendered from visible viewpoints, whereas Flex and OptCuts exhibit prominent cutting seams (highlighted in the zoomed-in insets of Fig. 5)
4.2 QUANTITATIVE EVALUATION
User Study. To evaluate semantic- and visibility of the our method, we conducted a user-preference study with 115 participants. We distributed the questionnaire to two groups: experts and general participants. Of the 115 participants, 70 are general participants (includ- ing graduate students with computer science and engineering backgrounds) and 45 are ex- perts: 31 software engineers, 3 project managers, product owners, 5 UV/layout artists, and 4 modeling artists, all working in the Media and Each participant completed 11 comparisons between textured 3D shapes and UV maps produced by our method, FlexPara, OptCuts, Autodesk Maya, Blender, and xatlas. For each comparison, partici- pants were rated each result according to three visual criteria: (i) texture-patters smoothness/continiiyy (ii) semantic alignment (how well colors correspond to meaningful parts), and (iii) seam visibility (how well seams are placed in occluded//ess-obvious re- gions). The semantic-aware and visibility-aware evaluations comprised 5 and 6 questions, respec- tively, for total of 11 comparisons per participant. Table 2 reports the percentage of expert participant preferences for each method. As the table shows, expert users, particularly UV, layout, and modeling artists, strongly prefer our proposed method over the baselines. Examples of questions in our user-study questionnaire are shown in Fig. 15 Also. Tiji
Entertainment industry for film and games.
Table 2:
To evaluate semantic- and visibility- awareness of the proposed method, we con- ducted user-study with 45 expert participants performing 11 comparisons between textured 3D shapes and UV parameterizations produced by our method and baselines. Our proposed method is strongly preferred by the expert users over the baselines.
Expert Preference Percentage
Viabiliy Awarenes Evalustion
OpeCus Li 2018
148 98.70 %
FlexPur Irigle
Oune- Vishilly-Anse Awoee Parm
Semantic- Aw arcuess Evaluation
sotion Jpay Pips
2.66 1s
Wkrnde lirhnn Arandation b025
5:33
Astodad ways (anmirst S
8:46
FlesPan: (muli-Crar
3.35
Oues Scnantic-Asee Teras
$0.22 %
Appendix A.33 reports the percentage of general participant preferences for each method. As shown in Table 10, general users also strongly prefer our proposed method over the baselines. Semantic- and Aisibllit-Awarenne
Evaluation. We also quantitatively evaluate both the semantic-aware and visibility aware components of our pipeline on a collection of representative meshes. For the semantic-aware pipeline we report (i) Hamming distance (lower is better). the frac- tion of vertices whose labels disagree after optimal label matching to the reference (we use SAMesh (Tang et al., (2024) outputs as ground-trutte truth): and (ii) Rand Index (RI) (higher is better), a pairwise agreement measure that counts how often vertex pairs are grouped or separated identically between the two labelings. For the visibility-aware pipeline we report Mean seam AO (lower is better in our AO convention, where 0 = occluded and 1 exposed), the average ambient-occlusion value over seam vertices, which indicates how well seams are placed in occluded regions. Tables T and summarize these metrics and compare our method against the baselines described in Sec. Further discussion of the results in these two tables are provided in Appendix A.3
9

---

Published as a conference paper at ICLR 2026
Geometry-Preee PrenetttrPrrssraatiion Evaluation. In addition to task-specific metrics mentioned above, in Ta- bles [and we evaluate geometric quality for all methods using conformaliry
and equiareality preeeepreservttion) (higher is better) to ensure the parameteriization remain faith- ful enough to the original surface. The proposed semantic-aware and visibility-aware UV parame- terization frameworks produce increase in standard distortion metrics including angle-distortion and area-distortion. This is a trade-off' which is expected when optimizing multiple objectives dur- ing the learning process. Specifically,, our optimization introduces explicit semantic-alignment and visibility
terms that encourage semantically consistent charts and reduce conspicuous methods (e.g., Autodesk Maya, Blender, xatlas, and FlexPara) focus only on traditional distortion objectives and therefore score better on those numbers, but they do not provide the semantic- and visibility-related guarantees produced by our method. We note that these semantic and visibility properties are often more valuable in some downstream tasks such as mass production, se- mantic editing, and 3D asset reuse, where con- sistent UV semantics and low seam visibility
seams. However, existing coometry-preserviee
Table 3: Quantitative comparison of the proposed semantic- aware UV parame- terization method against baselines on multiple evaluation metrics.
Method
Confecunalty
Equiareallt
Infercace Tims
Harnring Distance
Rand Infex
sathe
0,5995
0.9792
0.5341
Blend
Cantetiin 2005
0,8534
0.7173
0.5150
0.9033
May
0.125
039222
03746
0.998
0.9422
09392
0.2905
Settaric- Puritt
8:3188
0S151
03123
significantly reduce manual effort. Importantly, the trade-off is controllable in our framework: the relative emphasis between distortion and semantic/visibility objectives can be controlled by tunable weights and hyperparameters between different loss functions in Equations (I) and (8), so users can shift toward lower distortion when that is preferred.
Ablation Studies. To understand which design choices drive our results, we perform set of controlled ablations and summarize the key findings below (additional qualitative examples are in Fig. 13 and Fig. [4 in Appendix A..3p. (i) Partitioning strategy (ShDF vs. SAMesh). We re- place our default Shape Diameter Function (ShDF) partitioner (Shapira et al. 2008) with SAMesh Tang et al., 2024) while keeping the remainder of the partition-and canitiio-ann-aaae aaddparameterize pipeline identi- cal. Qualitatively both partitioners produce part decompositions that align with geometric structure across simple and complex models, resulting compact, low-distortion per-part UV charts (see the ap- pendix Fig. 13 and Fig. 14]. Quantitatively T Appendix. [A.3). using ShDF in our pipeline yields UV parameterization that are more conformal (better angle preservation) and more equiareal (better area preservation) than those produced when using SAMesh. (ii) Visibility loss (with withour). We ablate the proposed visibility-awan objective (S44 by training identical models with and without Lvs.- The visibility aware objective consistently lowers the mean AO of seam ver- tices (Fi Appendix. [A.3), i.e., it moves seams to regions of lower exposure (lower AO values). Qualitatively, models trained with the AO loss produce seams that are less salient in rendered images from canonical viewpoints (see Fig. 5). Importantly, as presented in Table. [ adding the visibility term does not noticeably degrade geometric distortion metrics, conformality sangle-preservatii and equiareality area- preservation). The ablations show that: (i) the ShDF partitioner is a semanti- cally reliable and geometry- initializer for our semantic-aware parameterization across diverse shapes; and (ii) the visibility-awar loss is required to effectively move seams to less-exposed re- gions without harming geometric quality. Additional ablation studies are provided in Appendix A.3
5 CONCLUSIONS
This study presents an unsupervised representatiion-laaring framework for semantic- and visibility- aware UV parameterization of 3D meshes. The semantic-aware pipeline leverages high-level mesh partitioning to align UV islands with meaningful 3D parts, producing UV charts that are easier to in- terpret and manipulate for downstream tasks such as texture synthesis. The visibility- pipeline uses ambient occlusion as a differentiable proxy for visual exposure to guide seam placement toward less-visible regions, reducing perceptual artifacts in texturing and rendering. Extensive qualitative and quantitative comparisons to state-of-the-art methods show our parameterizations achieve low distortion, stronger semantic alignment, and seams placed in perceptually favorable locations. We expect these contributions to serve as building blocks for generative 3D models and broader 3D content creation. Therefore, future work includes jointly learning UV parameterization and texture generation.
10

---

Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This research was supported by Autodesk Research and the Autodesk AI Lab. We especially thankful for the collaborative environment, technical resources, and insightful discussions that con- tributed shaping this work.
REFERENCES
Autodesk, Inc. Autodesk Maya (Version 2025.x). Autodesk, Inc., 2025. URL https::/wwwt
autodesk. htwp://ccccooooooocccs//aaaaaaa wwwe//aaaaavvviiiiivveeve Accessed: 2025-09-11.
Louis Bavoil and Miguel Sainz. Screen space ambient occlusion. NVIDIA developer information: http://developers. niiiia. com, 6(2):6, 2008.
Blender Foundation. Blender a 3D creation suite (Version 4.5). Blender Foundation, 2025. URL org Accessed: 2025-09-11.
httt:s::/wwwwble..
Zhiqin Chen, Kangxue Yin, and Sanja Fidler. Auv-net: Learning aligned uv maps for texture trans- fer and synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1465-1474, 2022.
Per H Christensen. Note# 35: Ambient occlusion, image-based illumination, and global illumina- tion. PhotoRealistic RenderMan Application Notes, 2002.
Dana Cohen-Bar, Daniel Cohen-Or, Gal Chechik, and Yoni Kasten. Tritex: Learning texture from a single mesh via triplane semantic features. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 21403-21413, 2025.
Sagnik Das, Ke Ma, Zhixin Shu, and Dimitris Samaras. Learning an isometric surface parameteriza- tion for texture unwrapping. In European Conference on Computer Vision, pp. 580-597. Springer, 2022.
Michael S Floater and Kai Hormann. Surface parameterization: tutorial and survey. Advances in multiresolution.fr geometric modelling, pp. 157-186, 2005.
Yanran Guan, Andrei Chubarau, Ruby Rao, and Derek Nowrouzezahraai Learning neural implicit representations with surface signal parameterizations. Computers & Graphics, 114:257-264, 2023.
Kai Hormann, Bruno Lévy, and Alla Sheffer. Mesh parameterization: Theory and practice. 2007.
Alec Jacobson, Daniele Panozzo, al. libigl: A simple C++ geometry processing library, 2018. https://liigll.iitub.ik
jpcy. xatlas: Mesh parameterization / uv unwrapping library. htttp:://github..cmm//pe xat as, 2025. Accessed: 2025-09-11.
Julian Knodt, Zherong Pan, Kui Wu, and Xifeng Gao. Joint optimization and texture baking. ACM Transactions on Graphics, 43(1):1-20, 2023.
Samuli Laine and Tero Karras. Two methods for fast ray-cast ambient occlusion. In Computer Graphics Forum, volume 29, pp. 1325-1333. Wiley Online Library, 2010.
Hayden Landis. Production-ready global illumination. Siggraph course notes, 16(2002):11, 2002. 2002.
Bruno Lévy. Sylvain Petitjean, Nicolas Ray, and Jérome Maillot. Least squares conformal maps for automatic texture atlas generation. In Seminal Graphics Papers: Pushing the Boundaries, Volume 2, pp. 193-202. 2023.
Minchen Li. Danny M Kaufman, Vladimir G Kim, Justin Solomon, and Alla Sheffer. Opteuts: Joint optimization of surface cuts and parameterization. ACM transactions on graphics (TOG), 37(6): 1-13, 2018.
11

---

Published as a conference paper at ICLR 2026
Minghua Liu, Mikaela Angelina Uy, Donglai Xiang, Hao Su, Sanja Fidler, Nicholas Sharp, and Jun Gao. Partfield: Learning 3d feature fields for part segmentation and beyond. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9704-9715, 2025.
Songrun Liu, Zachary Ferguson, Alec Jacobson, and Yotam Gingold. Seamless: seam erasure and seam-aware decoupling of shape from mesh resolution. ACM Trans. Graph., 36(6):216-1, 2017. 2017.
Morgan McGuire. Ambient occlusion volumes. In Proceedings of the 2010 ACM SIGGRAPII symposium on Interactive 3D Graphics and Games, pp. 1-1, 2010.
Ålex Méndez-Feliu and Mateu Sbert. From obscurances ambient occlusion: A survey. The Visual Computer, 25(2):181-192) 2009.
Anirban Mukherjee, Venkat Suprabath Bitra, Vignesh Bondugula, Tarun Reddy Tallapureddy, and Dinesh Babu Jayagopi. Semuv: Deep learning based semantic manipulation over uv texture map of virtual human heads. In International Conference on Computer Vision and Image Processing, pp. 112-127. Springer, 2024.
Alessandro Muntoni and Paolo Cignoni. PyMeshLab, January 2021.
Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger. Tex- ture fields: Learning texture representations function space. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4531-4540, 2019.
Matt Pharr, Wenzel Jakob, and Greg Humphreys. Physically based rendering: From theory to implementation. MIT Press, 2023.
Roi Poranne, Marco Tarini, Sandro Huber, Daniele Panozzo, and Olga Horkine-Hornung. Autocuts: simultaneous distortion and cut optimization for uv mapping. ACM Transactions on Graphics (TOG), 1-1,
Michael Rabinovich, Roi Poranne, Daniele Panozzo, and Olga Sorkine- Scalable locally injective mappings. ACM Transactions on Graphics (TOG), 36(4):1, 2017.
Nicolas Ray, Vincent Nivoliers, Sylvain Lefebvre, and Bruno Lévy. Invisible seams. In Computer Graphics Forum, volume 29, pp. 1489-1496. Wiley Online Library, 2010.
Pedro V Sander, Steven J Gortler, John Snyder, and Hugues Hoppe. Signal-specialized parametriza- tion. In Rendering Techniques, pp. 87-98, 2002.
Lior Shapira, Ariel Shamir, and Daniel Cohen-Or. Consistent mesh partitioning and skeletonisation using the shape diameter function. The Visual Computer, 24(4):249-25 -259, 2008
Alla Sheffer and John C Hart, Seamster: inconspicuous low-distortion texture seam layout. In IEEE Visualization, 2002. VIS 2002., pp. 291-298. IEEE, 2002.
Alla Sheffer, Bruno Lévy, Maxim Mogilnitsky, and Alexander Bogomyakov. Abf++: fast and robust angle based flattening, ACM Transactions Graphics (TOG), 24(2):311-330 2005.
Alla Sheffer, Emil Praun, Kenneth Rose, et al. Mesh parameterization methods and their applica- tions. Foundations and Trends@ in Computer Graphics and Vision, 105) 2007a.
Alla Sheffer, Emil Praun, Kenneth Rose, et al. Mesh parameterization methods and their applica- tions. Foundations and Trends@ in Computer Graphics and Vision, 105) 2007b.
Jason Smith and Scott Schaefer. Bijective parameterization with free boundaries. ACM Transactions Graphics (TOG), 34(4):1-9, 2015.
Olga Sorkine, Daniel Cohen-Or, Rony Goldenthal, and Dani Lischinski. Bounded-distortion piece- wise mesh parameterization. In JEEE Visualization, 2002. VIS 2002., pp. 355-362. IEEF, 2002.
Pratul P Srinivasan, Stephan Garbin, Dor Verbin, Jonathan T Barron, and Ben Mildenhall. Nuvo: Neural uv mapping for unruly 3d representations. In European Conference on Computer Vision, pp. 18-34, Springer, 2024.
12

---

Published as a conference paper at ICLR 2026
George Tang, William Zhao, Logan Ford, David Benhaim, and Paul Zhang. Segment any mesh. arXiv preprint arXiv:2408. 13679, 2024.
Fatemeh Teimury, Bruno Roy, Juan Sebastián Casallas, David MacDonald, and Mark Coates. Graphseam: Supervised graph learning framework for semantic uv mapping. arXiv preprint arXiv:201 1. 1.37448, 2020.
Toronto-based SideFX. Houdini a 3D animation software (Version 2/). foronto-based SideFX, Accessed: 2025-11 11-20.
2025. URL https: //wp:::/www/t iidexxx
William Thomas Tutte. How to draw a graph. Proceedings of the London Mathematical Society, (1):743-767, 1963.
Eric Veach. Robust Monte Carlo methods for light transport simulation. Stanford Univer- sity, 1998. URL htttps:://seeeeeee www.. httpa///...aac/o/o//oo//////a/// reacings veach thesis.
Jelle Vermandere, Maarten Bassier, and Maarten Vergauwen. Semantic uv mapping to improve texture inpainting for indoor scenes. arXiv preprint arXiv:2407.09248 2024.
Colin Weill-Duflos, David Coeurjolly, Fernando de Goes, and Jacques- Olivier Lachaud. Joint op- timization of distortion and cut location for mesh parameterization using an ambrosio-tortorelli functional. Computer Aided Geometric Design, 105:102231, 2023.
Fanbo Xiang, Zexiang Xu, Milos Hasan, Yannick Hold-Geoffroy, Kalyan Sunkavalli, and Hao Su. Neutex: Neural texture mapping for volumetric neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7119-7128, 2021.
Baixin Xu, Jiangbei Hu, Fei Hou, Kwan-Yee Lin, Wayne Wu, Chen Qian, and Ying He. Parameterization--riiee neural surface reconstruction for object-oriented editing in neural ren- dering. In European Conference on Computer Vision, pp. 461-479. Springer, 2024a.
Tian-Xing Xu, Wenbo Hu, Yu-Kun Lai, Ying Shan, and Song-Hai Zhang. Texture-gs: Disentangling the geometry and texture for 3d gaussian splatting editing. In European Conference on Computer Vision, pp. 37-53. Springer, 2024b.
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder via deep grid deformation. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 206-215 2018.
Qijian Zhang, Junhui Hou, and Ying He. Parapoint: Learning global free-boundary surface param- cterization 3d point clouds. arXiv preprint vrXiv:2403.10349 2024a.
Qijian Zhang, Junhui Hou, Wenping Wang, and Ying He. Flatten anything: unsupervised neural surface parameterization. Advances in Neural Information Processing Systems, 37:2830-2850, 2024b.
Yuming Zhao, Qijian Zhang, Junhui Hou, Jiazhi Xia, Wenping Wang, and Ying He. Flexpara: Flexible neural surface parameterization. arXiv preprint arXiv:2504. 19210, 2025.
Sergey Zhukov, Andrei lones, and Grigorij Kronin. An ambient light illumination model. In Euro- graphics workshop rendering techniques, pp. 45-55. Springer, 1998.
Guangyu Zou, Jiaxi Hu, Xianfeng Gu, and Jing Hua. Authalic parameterization of general surfaces using lie advection. IEEE Transactions on Visualization and Computer Graphics, 17(12):2055- 2014, 2011.
13

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 REMARKS ON SHAPE DIAMETER FUNCTION (SHDF)
Our semantic-aware parameterization, explained in See 3.3 relies on computing per-vertex
mantic partition of the input mesh using shape diameter function (Shapira et al. 2008). To estimate per-vertex semantic label, we design and implement method based on Shape Diameter Function Shapira et al. 2008) that involves the following key steps.
Compute per face local thickness (shape-diameter field. For a surface sample p (face centroid or vertex), the value (pDF(p) estimates the local object diameter by probing the interior along a cone centered on the inward normal at p. Let R(p) {r.]ez be set of rays radiating from p inside a cone of half-angle a around the inward normal. For each ray 7; we compute the length 4 of the intersection segment between T; and the mesh interior (i.e., the distance from p to the first opposite intersection). We robustly aggregate the ensemble { using a median (or trimmed mean) to obtain: (p) = median (fc)) {())).) This median- aggregation reduces sensitivity to spurious long short intersections caused by complex local geometry. In practice, to estimate scalar per surface element that measures local object thickness (intuitively, it presents the diameter of the object in the neighborhood of each sample), we use ray casting (using PyMeshLab (Muntoni & Cignoni 2021)) in multiple directions per sample and return a per-face scalar. Then, the raw scalars are normalized to [0, 1] and re-scaled via monotonic compression (e.g.. a log-like transform) to reduce dynamic range and improve cluster separability. In our implementation we use per-face ShDF evaluated face centroids with the following defaults: cone angle @ = 120 (as in the original work (Shapira et al. 2008)), number of rays R = 60, and ray sampling distributed uniformly within the cone.
Smoothing and normalization. Similar to (Shapira et al. 2008). we smooth raw ShDF samples on the mesh to promote spatial coherence and reduce pose-dependent noise. We apply small num- ber (e.g., 1-2) of iterations of Laplacian smoothing on the ShDF scalar field with area-weighted averaging. To balance values across scales and to boost contrast of small parts, we perform nor- malization:
log(1 + ShDF(p)) min, log (1 ShDF maxp log(1 + ShDF min, log(1 + (pDF(p))
Fit a Gaussian Mixture Model (GMM) to ShDF values. Given desired number of semantic components, to obtain soft class probabilities, we fit a one-dimensional Gaussian mixture model (GMM) to the per-face thickness scalars obtained from previous step. The probabilities from the GMM provide a data-driven per-face likelihood for each component and serve as the unary data term in the subsequent energy formulation.
Construct a boundary smoothness cost between adjacent faces. To bias the partition toward spatially coherent regions, we compute a smoothness cost on adjacent face pairs based on local geometry (for example, a function of the dihedral angle). Intuitively, nearly-coplane adjacent faces incur a low boundary penalty, whereas faces separated by sharp edges incur a higher penalty for being assigned the same label.
Initial partition assignment via data likelihood. As an initial labeling, each face is assigned to the GMM component with maximum probability (equivalently, minimum negative log- ligeeloodoood of the probability). This produces a purely data-driven segmentation that is generally noisy but captures the major mode structure of the thickness field.
Refine partitioning via energy-based graph cuts. The initial assignment, obtained from the previous step, is then refined using an iterative expansion-based graph-cut procedure that minimizes a global energy
E(labeling)
Σ Caata (face,, label) faces
Σ
Csmooth |labels differ),
(10)
adjacent Faces
where the unary term is derived from the GMM negative log- ligeliisss and the pairwise term is the smoothness cost described above. The expansion moves are solved by an optimal min-cut on an auxiliary graph for each label. Repeating this process across labels and iterating few times yields a stable and low-energy partition.
Re-label connected components. After refinement, a single label index can correspond to multiple disconnected components. To solve this issue, we relabel each connected component of faces that
14

---

Published as a conference paper at ICLR 2026
every final label corresponds to single connected sub-mesh. This guarantees that subsequent per- part processing operates on contiguous regions.
Post-processing. To produce clean 3D segmentation results, we apply simple post-processing heuristics: (i) remove tiny components below face-count threshold, (ii) smooth labels by majority filtering on local neighborhoods, and (iii) (when appropriate) merge adjacent labels that are seman- tically similar. These operations stabilize the partition and remove spurious tiny regions that would complicate per-part UV parameterization. The output is then a set of connected semantic sub-meshes {M&+ = (V&, F=) suitable for the per-part UV parameterization stage described in Sec 3.2
A.2 REMARKS ON BASE ARCHITECTURE FOR UV-PARAMETERIZAATOON LEARNING
Overview. Our backbone, based on bi-directional cycle mapping proposed in Zhao et al. 2025 implements a learnable and interpretable pipeline that mimics the artist operations used to create UV atlas: (i) deforming network, starting from a regular 2D grid of uniformly spaced coordinates, this network adaptively deforms the grid to produce a set of candidate UV coordinates that are potentially well-suited for mapping; (ii) wrapping network, this module maps 2D UV candidates, generated by the deforming network, to the 3D surface by smoothly bending and folding the de- formed planar grid until it conforms to the target geometry (in this case the input 3D mesh) which leads to effectively bridge the 2D and 3D domains; (iii) cutting network, operating directly on the 3D surface, generated by the wrapping network, this network transforms the input closed 3D geom- etry, obtained from the wrapping network, into an open, more developable 3D surface manifold. By strategically determining seam locations, i.e. where distortion reduction in subsequent flattening steps; and (iv) wnwrapping network, this final network projects the open 3D surface points back to the 2D domain, preserving smoothness and geometric coherence. In this design, each subnetwork is designed as a multilayer perceptron (MLP) applied pointwise to mesh vertices. The entire architecture is trained jointly in an end-to-end unsupervised fashion. The four subnetworks are assembled into two complementary branches that enforce both 2D-3D-2D and 3D-2D-3D cycle mappings, ensuring consistency across 2D and 3D domains. Cou- pled with differentiable loss functions designed to encourage bijectivity and minimize distortion, this backbone forms the base architecture of our method to achieve a candidate UV parameteriza- tion for the next stages. In the following, we detail on the loss designs and the interaction of the four sub-networks within the two-branch bi-directional cycle.
the input closed 3D) surface, it facilitates
Notation. Let M = FV,F) be a triangle mesh with vertex positions P E Vx3 and normals N E RVx3" Let G = {g:)< C IR2 denote a canonical planar lattice (the artist's canvas). The learnable UV mapping is un(-); the four MLP sub-networks are DeformNeet, WropNet, CutNet, UnwrapNeet
Sub-network Definitions. Each module is implemented as a small pointwise MLP We use additive residuals in the deform and cut modules to bias predictions toward small, interpretable offsets:
Q = DeformNettG) = G & GI),
(11)
[P,N] = WrapNett()) 6" (Q)
(12)
Pout CutNet(P) P5 (lo' Pl).
(13)
Qoyole Boood=Unwrappeee UnwrraNee((P.n) o. (Pont).
(14)
where Q are deformed 2D candidates, P are wrapped 3D points (with normals Ñ), Pout is the opened 3D surface after the learned cut offsets, Qeyele are the re-flattened coordinates produced by the UnwrapNet, d' (R++2) + RR? d'2 : IR2 + R², o, R2 + RRa, o" Rh+2 + R,, o" 124 + R33 o. R + hRth and du : 3 + R22 are implemented as stacked MLP layers.
Sub-network architectures. Each subnetwork is implemented as sequence of PWE blocks (point- wise Ix1 convolution with LeakyReLU activation functions), applied woint-wisely to per-vertex fea- tures. In practice each MLP is split into an encoding stage (MLP_1) and decoding stage (MLP 2.2) (e.g., Deform use [2 + 512 + 512 + 512 + 64] then (66 + 512 - 512 + 512 + 2 as encoder and decoder, respectively). DeformNet produces deformed UV candidates, WrapNet maps those
15

---

Published as a conference paper at ICLR 2026
Table 4: Layer-wiss wise summary of sub networks used our base neural architecture (PWE point- wise embedding conv 1d). Each listed MLP is sequence of PWE blocks; intermediate PWEs use LeakyReLU (with the negative slope of 0.01) while the final PWE is linear.
Subnetwork
Component
Layer channels
4PWE
Input / Output dim
Notes (activation)
DeformNet
MLP.1
1 -12
512 + 512
64]
4
2/61
LeakgReLUr0.00 un hidden PWEs: final PWE lincar LeakyReLU10.001 01) on hidden PWEs; tinal PWE linear
MLP 2
[00 - 511---2--200
512 512
4
66
WrapNet
MLP 1 MLP.2
-1 512
512
4
2/64
same as Deform MLP
[06 -11
-1-2---512
512
512
4
66/6
oupurss 6D coonds normals)
CutNer
MLP.1 1
[ 13+
-9
512
3
3/64
LeakyReLLU00.01 on hidden PWEs: final PWE linear prodices 3D offiess X., (then X, X X.)
MI.P 2
-
512
512
67/3
UnwrapNet
MLP
5-2-122
512
2
3/2
mags cut 30 points - unwrapped points
CuiNet
aboov
3/3
Unwrapping Cutting before this MLP
PWE (block) cone layer
Coew Contldkkemeeelly Cle - Cias
(per call)
point-wise ID conv bis optional I Let.
2D candidates to 3D points and normals, CutNet predicts pointwise 3D offsets to open the surface (making it more developable). and UnwrapNet applies CutNet followed by a final MLP to project the opened surface back 2D. All subnetworks operate pointwise (per-vertex), the final PWE in each MLP is linear, intermediate PWEs use LeakyRel.U as activation functions, and the entire ar- chitecture is trained end-to-end in an unsupervised manner (see Table4ffo a layer-wise breakdown).
Bi-Directional Cycle Mapping. We follow (Zhang et al. 2 Srinivasan et al. 2024 Zhao al.) 2025) to build our base neural UV parameterization architecture with two-branch MLP architecture that leverages the four sub-networks, explained above.
DeformNet Q WrapNet p CutNet, Peus UnwrapNeet Qcycle- Enforcing
2D-3D-2D: G
Q to be close to Qeycle stabilizes deformations and ensures that the learned 2D deforma- tions are actually realizable on the surface.
3D-2D-3D: CutNet, Peut. UnwrapNet Q WrapNet, P. Enforcing to close P constrains the unwrapping map so that the predicted UV coordinates can be re-embedded to match the true geometry.
Together the two cycles expose inconsistencies inclusing any seams, overlaps, stretches, and/or distortions as reconstruction losses that the network can reduce. We now detail all the reconstruction losses in detail.
Training losses. The backbone is trained with a weighted sum of unsupervised, geometry-awen aware losses:
L >wmp + Aeycle + Crppel Adis
(15)
Below the main terms.
Wrapping loss. Encourages P (Wrap-Net output) to approximate the input surface by computing the chamfer distance between the input mesh vertices P and reconstructed P.
S ChamferDistancoo(? P)+ Keurr (1 CosincSim N))
(16)
where the second term enforces vertex normal consistency by computing point-wise cosine similar- ity between normal vectors.
Cycle-consistency loss. Penalizes reconstruction error on both cycles by computing the squared norm 2 of the pair of deformed 2D candidates Q and the output of UnwrapNet Qeycte and the pair of input mesh vertices P and reconstructed vertices P from the WrapNet:
Leyck = I - Qoyotell 112 + 11P-P13
(17)
optionally augmented with normal/feature consistency terms.
16

---

Published as a conference paper at ICLR 2026
Anti-overlap (repulsion).
Discourages UV overlaps by repelling nearby vertex pairs in UV space:
reppe
1
Σ ((luo(()) - uo mi)llll
(18)
I
(i.))N
where N denotes local neighbor pairs, is hinge-style repulsion, and Iluv includes chart- periodicity handling.
Differential and triangle distortion losses. Control conformal and area distortion using a Jacobian-level differential loss (DDL) and triangle-based discrete loss (TDL):
Laist ADDL LDDL. + ATDL. LTDL
(19)
The DDL is computed by applying automatic differentiation to the MLP outputs to approximate the local Jacobian, while the TDL explicitly measures changes in angles and areas between 3D triangles and their corresponding 2D counterparts. In practice, we adopt the same formulations of DDL and TDL as proposed in (Zhao et al., 2025), , and refer readers to (Sheffer al. 2007b; (orrmann et al. 2007 Floater & Hormann concepts.
2005 for further details on distortion metrics and differential geometry
A.3 ADDITIONAL EXPERIMENTS AND RESULTS
We show expanded results, ablation studies, and discussions from the main paper featuring more surface parameterization and more viewpoints of different 3D objects in Figs. 6 15 and Tables. 5
9
Ablation studies on different UV packing (aggregator) methods. To further improve our semantic-aware UV parameterization pipelinc's output. a simple enhancement is to post-process the UV charts by jointly optimizing two key UV metrics: texel density and UV space utilization. Higher texel density improves the quality of the projected 3D texture, and maintaining consistent texel density across charts ensures uniform visual fidelity. At the same time, efficient space utiliza- tion maximizes the use of available texture memory. Hence, to analyze the effect of more advanced UV-chart packing (aggregator) strategies (different from the one proposed in Sec. 3.3 perform an ablation study and evaluate several UV-packing techniques from three widely used commercial tools including, Autodesk Maya (Autodesk, Inc. 2025). Houdini [Toronto-based SideFX 2025). and Blender (Blender Foundation (2025). We use a 3D cow mesh shown in Fig. to evaluate texel den- sity and UV-space utilization of UV parameterizations packed using different UV packing strategies from the three commercial tools. Specifically, we apply our semantic-aware UV parameterization framework as follows: (i) the 3D cow is first partitioned by PartField (Liu et al., (025) into seven semantic 3D parts, (ii) then the geometry-preserring
neural backbone is applied separately to each part to learn UV parameterizations for each 3D part, (iii) instead of using our default UV packing (presented in Sec. 3.3). apply different UV packing strategies from the three commercial tools, and (iv) lastly, for each packing strategy, we compute two UV metrics, namely texel density and UV-space utilization, for the UV parameterizations resulting from the three different UV packing methods. Table 5) reports the resulting values for both metrics to illustrate how each tool performs on 3D cow mesh example. Also, in Fig. 6]] we qualitatively compare the three commercial tools, applying their automatic packing and layout features to the same unwrapped mesh of 3D cow.
PartFicld). We replace our default Shape Diameter Function (ShDF) partitioner (Shapira et al. (0088) with SAMesh (Tang et al., 2024) and PartField (Liu et al. 2025) while keeping the remainder of the partition- marmtrraete (semantic-aware UV parametenization) pipeline identical. Then, we train each pipeline separately on input 3D meshes and measure and report presevvoennnntnoonn (conformality) and preserreesrvttoonn (equiareality) properties in the generated UV parameterization. Figs. 13 14] and Table the qualitative and quantitative results of these experiments, respectively. To inter- pret the numbers in the table, we point out two points: (i) There is obvious or general relationship between the choice of segmentation algorithm and the geometry--reservatee preservation metrics (conformality and equiareality) of the final UV parameterization. This is due to the fact that existing 3D segmenta- tion methods were developed to produce semantically meaningful parts, not to optimize downstream UV parameterization. Consequently, they do not incorporate UV-specific information. Therefore,
Ablation studies on different partitioning strategies (ShDF SAMesh
17

---

Published as a conference paper at ICLR 2026
Table 5: Quantitative ablations of different UV-packing strategies from three widely used commercial tools: Autodesk Maya, Blender, and Houdini. We use a 3D cow mesh shown in Fig. 6 to evaluate texel density and UV-space utilization of UV parameterizations packed using different UV packing strategies from the three commercial tools. Specifically, after applying our semantic-aware UV parameterization framework, UV parameterizations for each segmented 3D part, resulting seven UV charts. Then, instead of using default UV packing (presented Sec. B.3 we apply different UV packing strategies from the three commercial tools. Lastly, for each packing strategy. we compute two UV metrics, namely texel density and UV-space utilization, for the UV parameterizations resulting from the three different UV packing methods. Higher texel density improves the quality of the projected 3D texture, and maintaining consistent texel density across charts ensures uniform visual fidelity. the same time, efficient space utilization maximizes the use of available texture memory.
obtain
Method
Texel Density
UV Space Utilization
Chartl
Chart2
Chart3
Chart4
Chart5
Chart6
Chart?
Maya Autodesk, Inc.
2025
160.62
150.41
150.41
150.41
156.51
150.41
150.41
152.74
71%
Blender
Blender Foundation
2025
151.66
142.02
142.02
142.02
147.78
142.02
142.02
144.22
57%
Houdini
Torottobbsee Side 2025
157.08
147.09
147.09
147.09
153.06
147.09
147.10
149.37
62%
(a) Maya
(b) Blender
(c) Houdini
Figure 6: Qualitative ablations of different UV-packing strategies. further improve our semantic-aware UV parameterization pipeline's output, we present alternative more advanced UV packing results (diffcrent from the onc proposed in Sec. Scc.3.3 3.3 obtained from three widely used commercial tools: (a) Autodesk Maya, (b) Blender, and (c) Houdini. The left-most column shows the 3D cow mesh segmented by PartField (Liu et al. 2025 In the Autodesk Maya result (a), each UV chart corresponding its counterpart on the mesh. The same applies to Blender and Houdini results. Specifically, the 3D cow first partitioned by PartField (Liu et al. (2025) into seven semantic 3D parts. Then, the geometry preserving neural backbone (Sec. 3.2) is applied separately to each part to learn UV parameterizations for each 3D part leading to seven UV charts. Then, instead of using our default UV packing (presented in Sec. (.3). apply automatic packing strategies layout features of the three commercial tools the unwrapped mesh of 3D
semantic 3D part colored match
geometry-preserring
from the numbers in Table 6 we cannot draw a unified, causal conclusion connecting segmenta- tion method type final UV geometry properties. Having that said, include this segmentation ablation and Table intentionally to inform practitioners that different partitioning methods, while varying in semantic quality, can also influence gcometry-preserrane ing these results shows the important trade-off between semantic-awareness and geometric fidelity. Therefore, practitioners should try different segmentation approaches and choose the one that best fits their application needs. (ii) Of course, state-of-te the-thhtttt art, learning- based segmentation methods can produce more semantically coherent partitions than ShDF (Shapira al.) 2008) (as shown by works such as SAMesh (Tang et al.) (024)). Therefore, any improvements in that literature can di- rectly improve the semantic-awarent semantic-aware parameterization (Fig 2). In other words, better 3D partitioner is an immediate plug-in improvement for our method.
in the resulting UVs. Report-
of-te-o--the-as
our pipeline because segmentation is the first stage of our
Ablation studies on geometry-preservaaeee preservation vs. semantic / visibility objectives. Optimizing multiple geometry--reservation metrics simultaneously (conformality, equiareality, stretch, bijec- tivity, etc.) is known require trade-offs. Specifically, achieving the best possible value for every metric at once is generally impractical. Introducing higher-level objectives (semantic- and visibility- awareness), that propose in this work, increases this tension and therefore can further degrade
18

---

Published as a conference paper at ICLR 2026
some geometry metries. To quantify these effects, includ an ablation in Table. 7 that compares our proposed pipelines to the baseline (labelled "Base Neural UV Arch" in Table. 7 i... FlexPara) which is slate-of-the-art for geometry preservation. We compares three variants of our pipeline to the baseline: (i) Semantic-Aware Param, the pipeline of Sec. 3.3 (Fig. 2). which first partitions the mesh into semantic parts and then learns per-part parameterizations; (ii) Visibility-Aware Param, the pipeline of Sec. 3.4] (Algorithm. []. which produces a global atlas while steering seams toward Param, the combined pipeline that applies
less-visible regions; and (iii) Sssbility-SemanicAAAe both sets of objectives. Three observations explain the quantitative trends: (i) when comparing conformality and equiareality, the Semantic-Aware and the combined Desbblity+Semanti--waaa pipelines lose little equiareality relative to the baseline and even show slight improvement in con- formality in some cases. Intuitively, this is because the semantic-aware stage first partitions the mesh into multiple semantically meaningful subparts. Intuitively, learning a geometry-preserving preserving paramoterization on smaller, simpler patches is casier. In other words, the network can better satisfy geometry preservateenennn while also enforcing semantic consistency. Therefore, semantic partitioning provides a natural way to reduce distortion without heavily sacrificing geometry metrics. (ii) By contrast, the visibility-aware pipeline does not partition the mesh. Instead, it steers seams to less- visible (more occluded) regions while producing a global atlas (a single large chart). Unwrapping a whole 3D mesh into single UV chart to while simultaneously optimizing visibility and geometry is significantly harder. Consequently, the Visibility- ality compared to the baseline and compared the semantic-aware pipelines. (iii) The combined SesiliitySSmmntii--n-e
pipeline shows larger drop in equiare-
pipeline aims to obtain the best of both worlds: it first partitions the mesh into smaller parts to retain geometry preservation, and then steers seams toward less-visible regions within those parts. This combined approach yields a more favorable trade-off: it loses far less equiareality (0.6369) than the pure visibility-aware pipeline (0.6093) while still improving seam visibility and semantic consistency.
Analysis on training and inference computation times. To provide users and practitioners with some insights about the computation time needed for the proposed method in this study, we report both inference and training time of the proposed method in Table 3 Table [ Table g and Table [ and compare them with the computation time of the -tate-of-the-ard method (at the time of writing this manuscript), FlexPara (Zhao et al. 2025).
Inference time. (i) Visibility-aware pipeline: although training is performed per-mesh (as is common in this literature: Nuvo (Srinivasan et al. 2024). FlexPara (Zhao et al.] 2025), OptCuts (Li et al., 2018)), the visibility objective is applied only during training (not the inference stage). Specifically, at inference the trained model uses the same neural architecture as FlexPara (Zhao et al., 2025) and therefore incurs no additional runtime overhead. This is why the inference times for our Visibility-Aware Param and FlexPara are the same in Table [[ (=> seconds). (ii) Semantic-aware pipeline: the semantic-aware pipeline does introduce extra inference cost because it performs three stages at test time: (a) 3D semantic partitioning, (b) per-part parameterization (running the backbone once per part), and (c) aggregation/UV packing. These additional steps (segmentation, multiple backbonn invocations, and packing) explain the larger inference time reported in Table 2 and for the combined pipeline
in Table 1. A simple and effective mitigation is to parallelize the per-part backbone calls by using multi-threading during the inference, which would significantly reduce inference time.
Training time. (i) Semantic-aware pipeline (Table 8): counterintuttively, our semantic- aware pipeline achieves shorter training time than FlexPara (Zhao al. 2025) across dif- ferent 3D meshes. The reason is that the mesh is partitioned into many smaller submeshes: although our semantic-aware pipeline calls the backbone multiple times (once per seman- tic part), which might suggest longer training. this is compensated by the fact that the 3D segmented parts are much smaller than the original input mesh used by FlexPara. Hence, training on many small submeshes is faster than training single model on a large, com- plex mesh with many vertices and faces. This empirical observation is consistent with our careful time measurement of each stage in the semantic-aware pipeline which is: learn- ing UV parameterization for small 3D parts is much faster than doing the same for large meshes. More importantly, the reduction in per-part training cost outweighs the multiplica- tive overhead of multiple backbone runs, which results in shorter overall training time for the semantic-aware pipeline in our experiments. This is why Table 8 shows shorter train-
19

---

Published as a conference paper at ICLR 2026
ing times for our semantic-aware pipeline compared to FlexPara in experiments. (ii) Visibility-aware pipeline (Table 99: The visibility-awarr pipeline trains slower than Flex- Para (Zhao et al. 2025) across different meshes. This is expected because, compared to the base neural backbone (i.e., Paex)ara), our visibility-awan pipeline introduces per-iteration overheads: (a) per-vertex ambient occlusion (AO) computation, (b) candidate UV gener- ation via the backbone, (c) seam/boundary extraction in UV space, and (d) AO-weighted loss computation. The most expensive stage is seam extraction which accounts for approx- imately 90% of the additional training time. Specifically, for each vertex we inspect its 3-5 neighbors, compare mapped UV positions, and evaluate Eq. detect UV discontinuities. This neighbor-checking and distance computation scales with vertex count and dominates the additional training cost 90% of the overhead), making visibility-aware training signif- icantly heavier for complex meshes.
Potential attempts to overeome computation burdens. To mitigate the computational burdens discussed above, we propose two practical solutions: (i) parallelizing per-part backbone calls in semantic-aware pipeline. In our current implementation, the neural back- bone is executed sequentially for each semantic part. Switching to multi-threade or multi-GPU implementation to run per-part backbone calls in parallel would significantly reduce both inference and training times. (ii) Optimizing seam detection in visibility- pipeline. The seam-detection stage the main training-time bottleneck. Algorithmic and engineering improvements, e.g., spatial hashing, cached neighborhood lookups, and GPU- accelerated neighbor/distance checks, can significantly reduce this overhead. We note these optimizations straightforward improvements and sensible directions for future work.
Table 6: Ablation studies on the proposed partitioning strategy (ShDF vs. SAMesh vs. PartField). We replace our default Shape Diameter Function (ShDF) partitioner (Shapira al. 2008) with SAMesh (Tang et al. 2024) and PartField (Liu et al. 2025) while keeping the remainder of the partition-and-prren parametereee pipeline iden- tical. Quantitatively, using ShDF in our pipeline yields UV parameterizations that are more conformal (better angle preservation) and more equiareal (better area preservation) than those produced when using SAMesh.
Method
Conformality t Equiareality 1
Ours Semantic-Aware Param (PartField)
0.8999
0.6921
Ours Semantic-Aware Param (SAMesh)
0.8694
0.6270
Ours Semantic-Awar Param (ShDF)
0.9123
0.6707
preserving metries. Re- ported are mean Conformality and Equiareality (higher better). The Semantic-Awee Aware and the combined Visibility -eemnntic-Aware pipelines lose little equiareality relative to the baseline and even show slight im- provement in conformality in some cases. Intuitively, this is because the semantic-aware stage first parti- tions the mesh into multiple semantically meaningful subparts and the network can better satisfy geometry- preservation while also enforcing semantic consistency. However, the visibility-wware pipeline does parti- tion the mesh. Instead, steers seams to ress-lisible (more occluded) regions while producing one global atlas (a single large chart). Unwrapping and optimizing a single global chart to satisfy both geometry-presee preseevation and visibility objectives is significantly harder. Consequently, the Visibility-Awe Aware pipeline shows larger drop in equiarcality compared to the bascline. The combined the best of both worlds: partition the mesh into smaller parts retain geometry preservation, and (ii) steer seams toward less-visible regions within those parts. This combined approach yiclds favorable trade-off: loses far less equiareality (0.6369) than the pure visibility-aware pipeline (0.6093) while still improving seam visibility and semantic consistency.
Table 7: Ablation: the effect of semantic and visibility objectives
Semantic-Aware pipeline aims to obtain
Method
Conformality 1 Equiareality 1
Base Neural UV Arch.
0.9097
0.6759
Ours Semantic-Aware Param
0.9123
0.6707
Ours Visibility- Param
0.9175
0.6093
Ours Sisibbltyy-eemanticAAaare Param
0.9153
0.6369
20

---

Published as a conference paper at ICLR 2026
Table 8: Comparison of training time between proposed semantic-awan parameterization pipeline and
FlexPara's multi-chart parameterization (Zhao al. 2025). Total times shown outside the parentheses, and per-iteration times are shown inside.
Method
Training Time (Seconds)
Rabbit
Potion
Squidward House
Average
FlexPara (multi-Chart) Zhao al. 2025
900 (0.0900)
16377 (1.6377)
13784 (1.3784)
10353 (1.0353)
Ours Sewantic-Aware Param
676 (0.1352)
666 (0.1332)
602 (0.1 101)
648 (0.1296)
Table 9; Comparison of training time between proposed visibility-aware parameterization pipeline and
FlexPara's 's global (single-chart) parameterization (Zhao 2025). Total times are shown outside the paren-
theses, and per-itcration times are shown inside.
Method
Training Time (Seconds)
Duckic
Fish
FlipFlop
Rabbit
Spot
Average
FlexPara (single-Chart)
Zlao
2025
1306 (0.1 (.1306)
1323 (0.1323)
1275 (0.1275)
1293 (0. (0.1293)
1230 (0.1230)
1285 (0.1285)
Ours Visibility-Waare Param
36642 (3,6642) 47460 (4.7360)
16864 (3.3729) 35232 (2. 3488)
196008 (1. (609)
31 160 (3. (3.1160)
Table 10: To evaluate semantic- and visibility-awareed awareness of the proposed method, we conducted a user-study with 70 general participants (including graduate students with computer science and engineering backgrounds) performing 11 comparisons between textured 3D shapes and UV parameterizations produced by our method and baselines. We report the percentage of general participant preferences for each method. Our proposed method is strongly preferred by the general users over the baselines.
Method
General User Preference Percentage
Visiblity--Awarenes Evaluation
OptCuts (Li et al. 2018
1.43 %
FlexPara (single-Chame (Zhao et al. 2025
7.14 %
Ours Visibility-Wware Param
91.42 %
Semantic-Awareness Evaluation
xatlas (iccy 2025
4.28 %
Blender (Blender Foundation, 2025
4.28 %
Autodesk Maya (Autodesk, Inc. 2025
10
FlexPara (multi-Chart) (Zhao et al. 2025
7.14 %
Ours Gemantic-Awan Param
74.29 %
21

---

Published as a conference paper at ICLR 2026
Duckie
Cow
Nean KOwa
Seam Ad
Searn Near 2.627. Gibbal Mears EGGB
retsons
Sece tittoe
Ours
30
6. L-egoedd
Searn Peen AD 8-381, Clioa Mess AQwd 2036
Searn Vean AD 0.8022 Gotel Veen AO-0
Scar Mooan 40 1833, Chtud Moas AOwe B008
17.5
Seam variic
Staan vartics
11.0
12.5
FlexPara
33.0
whek Lemou
Ambirn docka -i-accided 2-mepoedd
Hhear tomppone))
Sear Moae 28747, Ditbel Moon ACva 8429
Mean AD Mean AOvd
verticss
OpiCuts
Amber Cosable Xevcchatet Inpgred
Octhiler Demomee
Arl Octaal Unooo Atd. Introted
Figure 7: Quantitative distribution of seam-vertex ambient occlusion (AO) for three meshes parameterized with method, FlexPara (Zhao al. 2025), and OptCuts et 2018). Red bars indicate AO distributions seam vertices, while black represent vertices. The y-axis denotes vertex density and the z-axis values. Our method shifts visibility-aww aware pipeline relocates seams to less visible (more occluded) regions, unlike FlexPara and OptCuts.
AO distributions toward lower values (left side), showing that the
B THE USE OF LARGE LANGUAGE MODELS
We used a large language model (LLM) to assist with editing, refining, and polishing the manuscript text (for example: abstract and captions). The LLM was used only for language refinement, improv- ing grammar, wording, concision, and overall readability, and did not contribute to the technical content, experiments, results, scientific claims. All suggestions generated LLM were manu- ally and carefully reviewed and, where appropriate, revised by the authors. The authors have full responsibility for the final text and for the accuracy the work.
22

---

Published as a conference paper at ICLR 2026
Rabbit
Cow
Fish
Duckie
Ours
FlexPara
OptCuts
Figure 8: Extended version of Fig. in main paper. Checkerboard texturing comparison using UV parame- terizations produced by our visibility- aaware method, FlexPara (Zhao et al. 2025). and OptCuts diel al. 2018) Each row shows rendered views of different meshes textured with checkerboard and magnified inset of visually important region near seams (red circles). Because method steers seams toward occluded regions, the checkerboard pattern appears substantially more continuous from typical camera viewpoints. By contrast, baselines exhibit visible seam artifacts in the zoomed-in in insets.
23

---

Published as a conference paper at ICLR 2026
Side 1 View
Side View
Front View
Top View
Back View
Bottom View
Ambient Occlusion
Ours
FlexPara
OptCuts
Ambient
Ours
FloxPara
OptCuts
Ambient
Ours
FloxPara
OptCuts
Figure 9: Extended version of Fig. in the main paper. Qualitative results for visibility-aware seam placement and UV parameterization three representative meshes. For each mesh, the top row shows per-vertex ambient occlusion (yellow exposed, purple accluded). Beneath the visualizations of cutting seams (red) from our method, FlexPara (Zhao al. (2025), and OptCuts (Li al., (018) (top to bottom). Our method places larger fraction of seam geometry in less-exposed regions, reducing the likelihood of visible seam artifacts under typical viewpoints.
24

---

Published as a conference paper at ICLR 2026
Side 1 View
Side 2 View
Front View
Top View
Back View
Bottom View
Ambient Occlusion
Ours
FlexPara
OptCuts
Ocolusion
Ambient
Ours
FlexPara
OptCuts
Figure 10: Extended version of Fig. 4 in the main paper. Qualitative results for visibility awaare seam place- ment and UV parameterization three representative meshes. For each mesh, the top shows ver-vertex ambient occlusion (yellow exposed, purple occluded). Beneath are the visualizations cutting seams (red) from our method, FlexPara (Zhao et al.) (2025), and OptCuts (Li et al. 2018) (top to bottom). Our method places a larger fraction of seam geometry in less-exposed regions, reducing the likelihood visible seam arti- facts under typical viewpoints.
25

---

Published as a conference paper at ICLR 2026
Gide View
9 V
Froee View
Top Mew
Back View
Detion View
UV WYPaneetertaec
Rushas
Vox JosAHO Berde themmtnn
Cun
Way Insuertte
Figure 11: Extended version of Fig. B in the main paper. Qualitative results of the proposed semantic-aware
parameterization method on Rabbit mesh. For each method, we show the rendered 3D objects from multiple viewpoints, with the corresponding UV atlas in the rightmost column. As shown, our method produces UV charts that are align more semantically with the mesh's 3D semantic parts, unlike the baselines.
26

---

Published as a conference paper at ICLR 2026
UV Parameterization
Side
Side View
Front View
Back View
UV Parameterization
Side 1 View
Side 2 View
Front View
Back View
Parameterization
Side View
Side View
Front View
Back View
UV Parameterization
Side View
View
Front View
Back View
Figure 12: Qualitative results the proposed semantic-aware UV parameterization method complex mesh objects with high number of triangle faces. For each method, we show the rendered 3D objects from multiple viewpoints. with the corresponding UV atlas in the rightmost column. As shown, our method is able to produce UV charts. for complex meshes. that are aliga semantically with the mesh's 3D semantic parts
27

---

Published as a conference paper at ICLR 2026
Side View
Side View
Front View
Top View
PartFleld
SAMeoh
Shape Diameter Function
PartField
SAMesh
Shape Diameter Function
PartFiold
SAMesh
Function
Figure 13: Ablation studies the proposed partitioning strategy (ShDF SAMesh vs. PartField) of sim- ple meshes. replace our default Shape Diameter Function (ShDF) partitioner (Shapira et al., 2008) with SAMesh (Tang et al. (024) and PartField (Liu et al.] (2025) while keeping the remainder of the partition-nde parameterize pipeline identical. Qualitatively all three partitioners can produce decompositions that align with geometric structure ever, seems that PartField produces more accurate and more detailed semantic segmentations than ShDF and SAMesh some cases (c.g., the feet and the tail are correctly detected, whereas they are not detected properly in SAMesh).
simple meshes, resulting compact, low-distortion per-part UV charts. How-
28

---

Published as a conference paper at ICLR 2026
Side View
Side View
Front View
Back View
UV Porameterizztiin
PariField
SAMeeh
Shape Diameter
PariField
Shape Diamster
Pardfield
SAMesh
Shape Diametor Function
Figure 14: Ablation studies on the proposed partitioning strategy (ShDF vs. SAMesh vs. PartField) com- plex meshes with high number of triangle faces. We replace our default Shape Diameter Function (ShDF) partitioner (Shapira et al. 2008) with SAMesh (Tang et al. 2024) and PartField (Liu et al, 2025) while keep- ing the remainder of the part decompositions that align with geometric structure across complex models. However, PartField appears to produce more accurate and more compact semantic segmentations than ShDF and SAMesh in some cases. For example, PartField correctly groups the entire set of fan blades into a single segmented part, whereas SAMesh and ShDF split each blade into separate part. This is not completely wrong, but it results in too many small charts after per-part UV parameterization, which may be undesirable for some downstream tasks.
pipeline identical. Qualitatively all partitioners produce
29

---

Published as a conference paper at ICLR 2026
Visiblitt-Awarreess User Study
on ties inager of auckon chour from dffiner plnes Eack se showe
whenh imagensn set lo met curee the rear vinble iis ie. chreevee poo thrugt unver thul
to (kg. tuckadis shadowetiacciidee regions, under tale, fartiidde
bilow mmages of con atowm from the each eachpanted udacketeard putton some the
Io the
1
3
2
Sernantcc-Awareees User Study
imager 1-0
ircapee
ding fomered
right
color sccurnicslyy eusniggt part (for example
inrt kips Lullens)
acamplee hwad wass
Wnrch set (1-5) looks moet semantically msandnngtu
in other words, which oar do the colore most clerryy map to meaningtt parts of the rabbit
otter veeds, in which our da calor moen cleatty in up mosii moahiightii parss of cow eoumphe teed man, body an, leys task wiltend salor aniiiing sentitennt and acorv uensitted sres??
sel 5i
meaningfa??
lng sart, head, body, tall) oehher than being scattened or spilt scroua unnelate amou??
2
Figure 15: Examples of four of the eleven questions our user-study questionnaire. To evaluate semantic-
and sabblitt--wwarenesss of the proposed method, we conducted a user- user-preference study with 115 participants. We distributed the questionnaire general participants (including graduate students with computer science and engineering backgrounds) are experts: 31 software engineers, project managers, 2 product owners, UV/layout artists, 4 modeling artists, all working in the Media and Entertainment industry for film and games. Fach participant completed 11 comparisons between textured 3D shapes and UV parameterizations produced by method, FlexPara (Zhao) al. 2225), OptCuts (Li al. 2018). Autodesk Maya (Autodesk, Inc. 2025). Blender (Blender Foundation 2025 and xadlas (jpcy) 2025). For each comparison, participants were rated each result according three visual criteria: (i) eexture-patters smoothness/coniinity, (ii) semantic alignment (how well colors correspond regions). to meaningful parts), and (iii) seam visibility (how well seams are placed in occluded/lcss-oving
groups: experts and general participants. Of the 115 participants, 70
45
The semantic-aware and visibility evaluations comprised 5 and 6 questions, respectively, for total of 11 comparisons per participant. Tables report the percentage of expert and general participant preferences for each method, respectively. by both expert industry-leved users and general users.
tables show, our proposed method strongly preferred over baselines
30


Reviews:
[{"review_id": "40bMABwBcz", "reviewer": "Reviewer_dJSn", "summary": "This work proposes a method for predicting a UV parameterization for a mesh that not only respects common geometric properties such as minimizing distortion and cuts, but also tries to keep semantically related components nearby in UV space and place cuts / seams in low visibility regions. To accomplish this, the method uses the ShDF to segment the mesh into semantic components and then uses ambient occlusion as a proxy for visibility. Each semantic segment is independently parameterized and then all segments are recombined and packed into a single atlas. The method is evaluated against several baselines both quantitatively with comparison figures and quantitatively using specific semantic and visibility metrics as well as standard distortion metrics. Ablations of several key method components are also included.", "questions": "- Based on my interpretation of eq 7 it seems the relationship is backward and the $AO_i$ should be $(1 - AO_i)$ in the numerator?\n- Have authors explored using other modern semantic segmentation methods? I.e Partfield [1]? I see authors have ablated the use of SAMesh but perhaps Partfield would work better. I wonder if on harder shapes learning based segmentation methods would work better than ShDF. Could the authors provide more intuition for why the ShDF segmentations produced more conformal and equiareal parameterizations?\n\nReferences: [1] Liu, Minghua, et al. \"Partfield: Learning 3d feature fields for part segmentation and beyond.\" Proceedings of the IEEE/CVF International Conference on Computer Vision. 2025.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- This work identifies a gap in the current state of parameterization methods. It then addresses this by proposing a method producing parameterizations that group semantic components and place seams at low visibility regions.\n- The soft seam membership introduced to make the cutting differentiable is an interesting contribution and can be used in future research aiming to take a learning based approach to UV parameterization.", "weaknesses": "- Distortion looks higher on this method in Fig 3 and is quantitatively worse in Tab. 1 and Tab. 2., indicating that this approach sacrifices distortion for semantics and less visible seams\n- As noted in this paper, a good parameterization is subjective and varies a lot artist to artist. Given this, it would be beneficial to include a user study where actual 3D artists can evaluate the quality of the parameterizations.\n- Quantitative metrics for seam visibility evaluate using the same ambient occlusion that this method optimizes for. While AO might be a good metric for visibility, it is likely not perfect in all situations and thus optimizing for it and then measuring on it feels unfair.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gHWv4B492y", "reviewer": "Reviewer_vZwh", "summary": "This paper presents an unsupervised, differentiable framework designed to automate 3D mesh parameterization (UV mapping), addressing this critical and often manual bottleneck in 3D content creation. The core contribution is augmenting standard geometry-preserving UV learning with two novel perceptual criteria: semantic awareness and visibility awareness.\n\nThe semantic-aware pipeline utilizes a partition-and-parameterize strategy, first segmenting the mesh into meaningful 3D parts using a Shape Diameter Function (ShDF), and then independently parameterizing and aggregating these parts into a unified atlas.\n\nConversely, the visibility-aware objective leverages ambient occlusion (AO) as a differentiable proxy for viewer exposure, actively steering the placement of cutting seams toward regions of low visibility. By minimizing this AO-weighted seam loss, the framework ensures that UV cutting seams are less likely to be observed, thereby reducing perceptible artifacts after texturing and rendering.\n\nOverall, qualitative and quantitative comparisons confirm that the proposed method generates UV atlases that maintain low geometric distortion while achieving better semantic coherence and superior, perceptually favorable seam placement compared to existing baselines.", "questions": "- The final stage of the semantic pipeline employs an \"intentionally simple and deterministic\" aggregation and packing strategy, placing UV islands into a $G\\times G$ regular grid. While this is simple and beneficial for texture editing and transfer workflows, the authors note it can be replaced by \"more advanced packing solvers.\" This choice implies the final UV atlas likely suffers from suboptimal UV space utilization or packing efficiency—a critical metric in 3D production that is not reported in the quantitative analysis.\n\n- Although the authors claim that adding perceptual objectives causes only \"minor changes\" in geometry-preserving metrics, the quantitative results show a noticeable drop in equiareality (area preservation) when the visibility objective is enabled—from 0.6759 (Base Neural UV Arch., i.e., FlexPara) to 0.6093 (Ours—Visibility-Aware Param). While the authors consider this “minor,” such degradation may be significant for applications requiring precise triangle area preservation (e.g., lightmap baking). The paper does not provide an in-depth analysis of whether this loss of geometric fidelity is the minimal necessary cost to achieve perceptually superior seam placement.\n\nCould the authors provide more in-depth analysis and discussion on the above two points?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is easy to follow. The framework successfully transitions from the standard foundation of geometry-preserving mesh parameterization learning to its specific novel contributions: semantic awareness and visibility awareness.\n\n- The research includes comprehensive comparisons against state-of-the-art baselines, encompassing both recent open-source learning methods (e.g., FlexPara, xatlas) and established industrial software tools (e.g., Autodesk Maya and Blender’s SmartUV)\n\n- The proposed semantic-aware and visibility-aware parameterizations are effective. \n\n  - The semantic-aware pipeline generates practical, high-utility UV atlases by employing a partition-and-parameterize strategy based on the ShDF. This produces UV charts that align with meaningful 3D parts, making them cleaner, more interpretable, and directly beneficial for downstream tasks such as texture painting, editing, and transfer. \n  - The visibility-aware objective significantly improves perceptual quality by actively minimizing an (AO–weighted loss, successfully steering cutting seams toward less-exposed, more occluded regions. Using AO as a proxy supervision signal is a concise and elegant way to control visibility.", "weaknesses": "- The methodology’s initial phase, \"geometry-preservation mesh-parameterization learning\" (Sec. 3.2), adopts a network architecture based on the bi-directional cycle mapping backbone proposed in prior work (e.g., Zhao et al., 2025). This phrasing may lead readers to believe Stage I is a novel geometry parameterization method, rather than emphasizing that the true contribution lies in integrating semantic-aware and visibility-aware objectives as downstream tasks atop an existing geometric backbone. The manuscript could improve clarity by explicitly framing Stage I as an adoption of a known geometry-preserving backbone (e.g., FlexPara) and focusing the novelty on the downstream integration of the semantic and visibility objectives.\n- The ablation comparing the Shape Diameter Function (ShDF) partitioner to the SAMesh baseline is confusing. Quantitatively, SAMesh output is treated as GT for semantic metrics (Hamming Distance and Rand Index). Yet, the ablations show that the proposed ShDF partitioner yields UV parameterizations that are geometrically superior (more conformal and more equiareal) than those using SAMesh. Since ShDF is primarily geometry-driven (local thickness), the paper lacks external validation against industry-standard or human-annotated semantic datasets (e.g., PartNet, PartNeXt) to confirm whether ShDF’s partitions align with true human semantic decomposition.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "APiYivxP5S", "reviewer": "Reviewer_fuqi", "summary": "This paper proposes a fully differentiable and unsupervised framework for 3D mesh parameterization that jointly accounts for semantic consistency and visibility awareness.\nThe method introduces two key objectives:\n\n(1) a semantic-aware objective, which leverages the Shape Diameter Function (ShDF) to segment the mesh into semantically meaningful regions and then learns UV parameterizations for each region in an unsupervised manner; \n\n(2) a visibility-aware objective, which employs a differentiable Ambient Occlusion (AO) loss to guide seam placement toward less visible areas, thereby improving texture continuity and perceptual quality.\n\nExperimental comparisons with FlexPara, OptCuts, Blender SmartUV, Maya, and xatlas show improvements in semantic alignment, seam invisibility, and geometric distortion.", "questions": "The method for determining cut seams appears to be crucial in the visibility-aware UV parameterization, yet Eq. (5) does not clearly explain how the seams are effectively identified. Could the authors elaborate on how this formulation contributes to seam detection and clarify its effectiveness in guiding the visibility-based UV cutting process?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper is well motivated, aiming to address the challenges of 3D asset generation and to narrow the quality gap between generated assets and artist-created 3D models.\n\n2.The use of off-the-shelf tools to obtain part segmentation information for UV parameterization is both reasonable and effective.\n\n3.Leveraging geometric properties, such as Ambient Occlusion (AO), to determine UV cut seams in a differentiable manner is novel and demonstrates clear effectiveness.", "weaknesses": "1.The authors should clarify their specific contributions, as providing coarse semantic information and applying an alpha-cut strategy to optimize per-vertex or per-face labels have already been explored in prior 3D segmentation works. Moreover, the UV parameterization learning approach appears largely adapted from existing methods.\n\n2.Since the semantic partitioning relies on off-the-shelf tools and involves hard segmentation of the mesh into parts before subsequent processing, it is difficult to regard the proposed method as a fully differentiable framework.\n\n3.The proposed UV chart aggregator is straightforward but not very effective. It can lead to significant wasted space on UV maps and may compromise several desirable properties of UV parameterization, such as continuity and uniform distortion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0WVhEpp70F", "reviewer": "Reviewer_Gh6M", "summary": "The proposed method builds upon previous work, such as FlexPara and Nuvo, which address UV unwrapping. These methods propose a neural network optimization-based unwrapping approach with mapping cycles. The neural networks employed are neural fields similar to those utilized in NeRF. Compared to previous methods, the proposed approach incorporates more meaningful cut control based on visibility and semantics. This significantly improves the cut positions and generates more meaningful islands. For semantics, the authors propose using the shape diameter function (ShDF), while also demonstrating a version employing a semantic network. Regarding visibility-based cutting, they propose utilizing the ambient occlusion (AO) as a measurement. Both losses can be utilized jointly during the optimization process.", "questions": "- Could you please provide the training duration per mesh? n Table 2, there is an inconsistency in the inference times. Since training is performed per asset, it should be factored in. FlexPara estimates times of approximately 400 seconds for a global unwrap and 1300 seconds for a multi-chart, which is comparable to Nuvo’s speed. I would anticipate that the method would require slightly longer training times.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The utilization of AO as a measurement to identify concealed cut areas is a novel approach. The authors’ integration of this information (soft-selection) into the optimization process is noteworthy.\n- Parallel to the AO integration, the incorporation of part segmentation employing ShDF and Gaussian mixture models to learn the soft assignment is also interesting.\n- While the SAMesh version appears more preferable to me from the supplementary materials, the application of ShDF yields remarkably convincing results, considering the simplicity of the technique.\n- The authors comprehensively evaluate their method employing various metrics, demonstrating the enhanced part segmentation.", "weaknesses": "- The current level of UV unwrapping is still not on artist-levels, but it does make significant improvements over previous works.\n- The chart layout is intentionally straightforward, but I could envision easy modifications to maintain a similar texture density.\n- Overall, the method only adds a small delta on-top of the existing methods (Semantic and Visibility-based cutting).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)