ArtUV: Artist-style UV Unwrapping
Authors: Yuguang Chen
Xinhai Liu
YANG LI
Victor Cheung
Zhuo Chen
Dongyu Zhang
Chunchao Guo
Venue: ICLR 2026 Poster (2026)
Keywords: UV unwrapping
Artist-style
Auto-Encoder
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A learning-based UV unwrapping method that can automatically generate artist-style UV maps in seconds.

Abstract: UV unwrapping is an essential task in computer graphics, enabling various visual editing operations in rendering pipelines. However, existing UV unwrapping methods struggle with time-consuming, fragmentation, lack of semanticity, and irregular UV islands, limiting their practical use. An artist-style UV map must not only satisfy fundamental criteria, such as overlap-free mapping and minimal distortion, but also uphold higher-level standards, including clean boundaries, efficient space utilization, and semantic coherence.
We introduce ArtUV, a fully automated, end-to-end method for generating artist-style UV unwrapping. We simulates the professional UV mapping process by dividing it into two stages: surface seam prediction and artist-style UV parameterization. In the seam prediction stage, SeamGPT is used to generate semantically meaningful cutting seams. Then, in the parameterization stage, a rough UV obtained from an optimization-based method, along with the mesh, is fed into an Auto-Encoder, which refines it into an artist-style UV map. Our method ensures semantic consistency and preserves topological structure, making the UV map ready for 2D editing. We evaluate ArtUV across multiple benchmarks and show that it serves as a versatile solution, functioning seamlessly as either a plug-in for professional rendering tools or as a standalone system for rapid, high-quality UV generation.

Published as a conference paper at ICLR 2026
ARTUV: ARTIST-STYLE UV UNWRAPPING
Yuguang Chen
Xinhai Liu
Sun Yat-sen University
Tencent Hunyuan
Tencent Hunyuan
adlerxhliu @ @tencent.com
Yang Li
Victor Cheung
Tencent Hunyuan
Tsinghua University
liyang @ mi.tuutooyoo.c.ip
Tencent Hunyuan
wikizhang @ tencent.com
Zhuo Chen
Dongyu Zhang
Tencent Hunyuan zhuooochen @ tencent.com
Sun Yan-sen University zhangdy27 @ mail.sysu.edu..n
Chunchao Guo
Tencent Hunyuan
chunchaoguo eactaaaogu@@gmill.ccmmm gmail.com com
ABSTRACT
UV unwrapping is an essential task in computer graphics, enabling various vi- sual editing operations in rendering pipelines. However, existing UV unwrapping methods struggle with time-consuming. fragmentation, lack of semanticity, and irregular UV islands, limiting their practical use. An artist-style UV map must not only satisfy fundamental criteria, such overlap-free mapping and minimal distortion, but also uphold higher-level standards, including clean boundaries, ef- ficient space utilization, and semantic coherence. We introduce ArtUV, fully automated, end-to-end method for generating artist-style UV unwrapping. We simulates the professional UV mapping process by dividing it into stages: surface seam prediction and artist-style UV parameterization. In the seam predic- tion stage, SeamGPT is used generate semantically meaningful cutting seams. Then, in the parameterization stage, rough UV obtained from an optimization- based method, along with the mesh is fed into an Auto-Encoder, which refines it into an artist-style UV map. Our method ensures semantic consistency and preserves topological structure, making the UV map ready for 2D editing. We evaluate ArtUV across multiple benchmarks and show that it serves as versa- tile solution, functioning seamlessly as either a plug-in for professional rendering tools as standalone system for rapid, high-quality UV generation.
INTRODUCTION
UV parameterization (Floater & Hormann 2005 Sheffer et al. (007), also known as UV unwrap- ping, is a fundamental task in computer graphics that plays an essential role in modern rendering pipelines. This process serves as the foundation for numerous downstream applications, includ- ing texture editing and lightmap generation. UV unwrapping specifically involves establishing mapping from each 3D vertex (z, of mesh corresponding 2D coordinates (u, v) in UV space while maintaining the topological connectivity between vertices. To be effective in practice, high-quality UV unwrapping requires both basic criteria such as low distortion and no overlaps, and higher-level criteria including clean boundaries, space efficiency, and semantic coherence.
"Corresponding author

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Published as a conference paper at ICLR 2026
Input Mesh
Surface Cut
Init Map
ArtUV Map
Scamo
Antiy Parameter
Figure I: ArtUV pipeline: First, SeamGPT is used to predict the semantically meaningful seams on the input mesh surface. Based on these cutting line, the mesh is segmented into charts. For each chart, Ministretch-Unwrap (Rabinovich al., 2017 Jiang et al., 2017 Lévy al., (202)) is used to quickly generate the initial UV map. This initial UV map is then input into the ArtUV parameterization module Optimize the artist-style UV map.
UV unwrapping methods can typically be categorized into three types: top-down, bottom- up (Sorkine et al., 2002; Li et al. 2018), and learning-basee based (Srinivasan al.] 2024 Zhang al.] 2024). The top-down method begins by identifying seams on the 3D object to partition the surface into separate charts. These individual charts are then unwrapped with careful attention mínimiz- ing distortion while preserving neat boundaries. The process concludes by efficiently packing these UV islands into a cohesive, complete UV map. Conversely, the bottom-up approach starts with the entire object surface as discrete elements, then progressively merges these triangles by optimizing an energy function until convergence produces the final UV map. Learning- based methods employ unsupervised training through a cyclic mapping network. This architecture projects the 3D object into UV space and reconstructs it back to 3D coordinates, forming a complete round-trip transfor- mation. During this process, physical constraints such mapping bijectivity guide the network optimization. Once trained, the forward 3D-to-2D pathway directly generates the UV unwrapping.
Traditional top-down methods demand considerable time and expertise, requiring experienced artists to determine optimal cutting locations and manually adjust UV islands after unwrapping. This re- liance on manual intervention makes challenging to achieve the optimal balance between distortion and neatness. Bottom-up methods face a different issue: their discretization and re- re-clustering pro- cesses often produce fragmented UV maps that compromise the neatness of individual UV islands. Meanwhile, current learning-based methods require extensive training time for each 3D model and typically rely on point cloud inputs to handle diverse formats. This approach destroys the topo- logical relationships between points, creating chaotic UV mappings with significant overlap that severely limits practical usability. More fundamentally, both bottom-up and learning-base -baend meth- ods lack semantic awareness. For character models, artists prefer semantically meaningful divisions where limbs and torso are unwrapped separately, as this greatly facilitates subsequent texture editing tasks. Without this semantic understanding, these automated methods fail produce the intuitive, artist-friendly results that practical workflows demand.
In this paper, we introduce learning-basee based top-down method for UV unwrapping, decomposing the task into surface seam prediction and UV parameterization. For seam prediction, we leverage ScamGPT (Li et al. 2025) semantically segment the original mesh surface. Then, in the UV parameterization component, we employ an Auto-Encoder model to simulate the manual adjust- ment process of UV mapping. Specifically, we encode the original mesh information and constrain the output UV map in terms of its neatness, overlap, and distortion. After extensive training with carefully curated data, we obtain a general UV unwrapping method capable of optimizing the ini- tial UV map from traditional modeling software into one that aligns with artist-style UV mapping. Ultimately, we present fully automated, end-to-end UV unwrapping method that generates high- quality, artist-style UV maps with semantic information in seconds.
Extensive experiments demonstrate that our method outperforms traditional and learning- based UV unwrapping methods. Our main contributions are follows:
We propose AnUV, a novel dearning-bssed framework that automatically generates artist-style UV maps from 3D meshes with semantic awareness and professional quality standards.
We present AnUV-200K: a high-quality dataset comprising 200K artist-style UV maps, filling the gap in datasets specifically focused on high-quality UV maps.
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Comprehensive evaluations demonstrate that ArtUV significantly outperforms satte-of-the-are UV unwrapping methods across distortion, utilization, and processing speed metrics.
RELATED WORK
2.1 OPTIMIZATION BASED UV UNWRAPPING METHOD
Traditional UV unwrapping methods primarily rely on numerical optimization techniques that aim to minimize specific energy functions when mapping 3D mesh surfaces to 2D parameter domains, forming the core of both early and many contemporary unwrapping tools. These methods can be classified into two categories based on whether surface seams are pre-defined: For methods with given seams that partition the mesh into separate charts, LSCM (Lévy et al. 2023) and ABF++ (Sheffer al.] 2005) optimize angles in the 2D domain to approximate their 3D coun- terparts, theoretically achieving conformal mapping with minimal angular distortion, though often struggling guarantee global bijectivity. Conversely, approaches such as SLIM (Rabinovich al.) 2017]. SCAF (Jiang et al. 2017). and /mmih & Schaefer (2015) initialize UV maps as bijective map- pings and explicitly incorporate local/global overlap constraints during optimization to maintain bi- jectivity. While these methods typically produce low-distortion results, they face inherent challenges in simultaneously optimizing for both boundary regularity and distortion minimization. For methods without pre-defined seams that jointly optimize surface cutting and parameterization, (Sorkine et al.) 2002) discretizes surfaces into compact triangular patches that iteratively grow around seed triangles until meeting termination criteria, while OptCuts (Li et al.) 2018) proposes joint energy function minimizing both seam length and distortion for synchronous optimization. However, such joint op- timization algorithms often yield impractical UV maps due to excessive fragmentation or lack of semantic coherence. Consequently, current production pipelines still require substantial manual in- tervention, including hand-placed seams and manual UV island adjustments, to meet professional rendering requirements.
2.2 LEARNING BASED UV UNWRAPPING METHOD
With the advancement of deep learning techniques, learning-based UV unwrapping methods have emerged as a research focus. Nuvo (Srinivasan et al. 2024) employs a multi-category neural net- work architecture separately handle mesh segmentation and parameterization, utilizing multiple loss functions to enforce bijectivity and minimize distortion. FAM (Zhang et al. 2024) introduces mhysically-inspire framework comprising sub-networks for surface cutting. UV deformation, wrapping, and packing, achieving point-to-point mapping for arbitrary 3D representations through bidirectional cyclic mapping mechanism. However, these methods exhibit notable limitations: the absence of semantic and requiring time-consuming per-scene optimization. Particularly, FAM's point-cloud-bases approach disrupts topological structures and generates irreparable overlapping artifacts. Most critically, these algorithms struggle to achieve the neatness and aesthetic quality of artist-style unwrapping. resulting in suboptimal UV utilization. As a result, they are impractical for use in professional rendering pipelines.
2.3 CUTTING-SEAM PREDICTION METHOD
The UV unwrapping process typically involves surface cutting and parameter mapping. Recent ad- vances in deep learning have inspired several works (Wang al. 2020 Bazazian & Parés 2021 Himeur et al. 202T) that employ neural networks for edge point detection, framing it as a per-poin classification task. EC-Net (Yu et al.) 2018) reformulates this approach
regression problem by learning residual point coordinates and point-to-edge distances to identify edge points more pre- cisely. Building upon these developments, SeamGPT (Li al. 2025) innovatively simulates pro- fessional workflows by utilizing an autoregressive network to model surface cutting as a next-cut- point prediction task, enabling semantically meaningful cuts for both artist-created and Al Al-generated meshes.
DATA PREPARATION
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We collected UV-mapped mesh models from mul- tiple open-source 3D datasets, including Obja- verse (Deitke al.] 2022), Objaverse-XL (Deitke et al., 2023), and 3D-FUTURE (Fu et al., 2021). focusing artist-style UV maps. To ensure high- quality training data, we implemented a rigorous fil- tering process. From an initial collection of approx- imately 350,000 textured mesh models, we first de- composed the complete models into individual UV islands and then filtered out cases with overlapping UV maps or excessive fragmentation (fewer than 5 vertices per island), yielding refined subset of around 300,000 independent UV islands. As illus- trated in Figure 2 vertex count distribution analysis indicated that over 97% of these islands contained fewer than 500 vertices; larger islands were excluded
1.75
27.7%
1.00
1.2%
1.04
0.00
0:50
50-000
100-200 200-300 300 020-00 400 500 500-1000 3000. Vert_coure Ranges:
Figure 2: Distribution of vertex count in ArIUV-200K.
to enhance training stability and mitigate memory overhead. We then manually selected UV islands with meaningful semantic information and well-organized layouts. Each candidate processed using Anhistretch-Unwer method and evaluated against the original UV maps using SSIM (Sara al. 2019), with islands scoring between 0.5 and 0.8 selected as high-quality, manually adjusted cases.
Ultimately, we construct ArtUV-200K, high-quality benchmark dataset for artist-style UV unwrap- ping, containing approximately 15,000 objects and 200k UV islands.
METHOD
Output
NLF
Linaras
Crark
XX2
Figure 3: ArtUV parameterization architecture. Res-M MLP: Performs importance-baser di- mension mapping of input parameters; SAGEConv: Fuses local features between adjacent vertices via graph convolution; Pyramid ED: Enables global vertex interaction through attention encoder and coarse-to-oe fine decoder; Output: Combines predicted offsets with initial map for final UV parameter- ization.
We present AnUV, a novel automatic artist-style UV unwrapping approach that incorporates both surface cutting and UV parameterization. For the surface cutting, we replicate SeanGPT, ad- vanced auto-regressive (Lütteephh] 2013) seam prediction model. For UV parameterization, we propose groundbreaking artist-style UV parameterization model based
Auto-Encoder.
In the following sections, Sec. 4.1 will first provide a concise overview of SeamGPT. Next, in Sec. 4.2 we present our artist-style UV parameterization method, where we formulate the artist- style UV unwrapping task as learning the discrepancy between traditional software- aettarr-ggenerted UV maps and artist-optimized UV maps. Finally, in Sec. 4.3] will focus the loss function in parameterization module, which enables the model to generate UV maps that are as neat and low- distortion as those created by artists.
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4.1 PRELIMINARIES
Given a 3D mesh model M. which includes a vertex set V E RNx3 and a triangle face set F RMXx3, the surface cutting task is identify cut vertices V. C V that form connected seams along mesh edges, partitioning the surface into discrete charts. SeamGPT formulates surface cutting as a sequence prediction problem by spatially sorting and quantizing cut vertices, where each token represents a coordinate value and six consecutive tokens define a seam segment. Specifically, for an input 3D mesh, SeamGPT first samples point clouds on vertices and edges and compresses them into a latent shape condition using point cloud encoder from Zhao et al. (2025). Next, follow- ing Hao et al. (2024), SeamGPT builds an hourglass-like autoregressive decoder with multiple Trans- former (Vaswani et al.) 2017) stacks at each level, which bridges these hierarchical stages through downsampling and upsampling layers. The decoder autoregressively generates coordinate tokens starting from SOS until EOS, with final seam vertices obtained by nearest-poing projection of discrete tokens onto the mesh surface.
4.2 ARTUV PARAMETERIZATION
Modeling. UV parameterization aims establish a continuous bijective mapping from 3D mesh M to 2D plane P E 1]. The corresponding point set on the plane is represented as Q E RNX2, The most straightforward approach would be directly use all mesh information (Iu F,N, C, D)), including vertices V. faces F, normals N, degrees C' and curvature D, as input and we could then train powerful model learn the 3D-to-21D mapping. However, extensive empirical evidence has shown that despite having groundtruth, directly learning the mapping process a challenging task. Moreover, a simple projection is not our goal; our ultimate aim is to obtain a neat and low distorted artist-style UV map. Therefore, inspired by traditional top-down modeling process, we utilize the UV map Q; obtained through an optimization- based unwrapping method as initialization, enabling the parameterization model to learn how adjust Qi into an artist-style UV map. We additionally include Q; as part of the input and the final input represented as T = Tas UQ:- Next, we use the model predict the offset Qo required for each vertex in the initial UV map during the manual adjustment process. By adding the predicted offset Qo to Qs, we obtain the final UV map Qpred= = Q; Qo which satisfies the artist's standards.
Architecture. The architecture of our parameterization model is illustrated in Figure 3 First, we devise a residual MLP module (Res-M MLP) with adaptive dimension mapping, which dynamically adjusts feature dimensions according to their empirically observed importance in UV unwarpping tasks (Q,
N D), where the residual structure effectively preserve essential in- put information while enhancing feature representation. To maintain topological consistency, we construct a graph structure with vertices as nodes and face adjacency as edges, employing SAGE- Conv (Hamilton et al., (017)) for local feature propagation among neighboring nodes. The processed features are then fed into a Pyramid ED module, where stacked attention layers in the encoder en- able global vertex feature interaction, followed by coarse-to-fine decoder that concurrently extracts coarse- coarse-grained global structure and fine- mne-grained local details to predict UV space offsets Qo. This predicted offset is then added to the initial UV coordinates Q,, resulting in the final predicted UV map Q preed-
4.3 LOSSES
To generate UV maps that align with the artist's design standards- namely neatness, minimal distortion, and free overlap, we optimize the UV parameterization model's performance through multi-term weighted loss function.
Aligoed Q
The fundamental component the UV reconstruc- tion loss, which directly measures the positional dis- crepancy between predicted UV coordinates Qpred and groundtruth coordinates Qtt- Before computing this loss, a critical preprocessing step is performed to align the ini- tial UV coordinates Q; with the groundtruth Qge in ro- tation space. As in Figure 4 using Hom'' method (Hom) 1987), which computes
Rotation
this alignment achieved
Figure 4: UV map alignment.
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optimal rotation matrix R to align the two sets of coordi- nates by minimizing their rotational discrepancy. Specif- ically, The covariance matrix W between Qi and Qgt is computed as:
N
W
qa 7 (p: p) Σ
(1)
where q and are the corresponding points in Q; and Qgt and i and p are their mean values.
Then, Singular Value DScompositton(SVVI)) is applied to W to obtain the optimal rotation matrix R:
W UEV
1 0 sign(det(U)- det(V
0
$
(3)
R USVT
(4)
This rotation matrix R used to align the initial coordinates Qi with the ground truth coordinates Qst- After the alignment, the reconstruction loss is computed as the pointwise L1 distance between the predicted and ground truth coordinates:
Lirecon 1Utt Pprealli
To further enhance the structural regularity of predicted UV maps to match manually unwrapped results, we introduce an belgeepreeeriigg silhouette loss. This is implemented through differentiable rendering of UV map silhouette in Figure [5] By computing the L2 distance between silhouette maps predicted and ground truth UV map. the model is guided to focus on boundary information that significantly reflects the neatness of UV islands, expressed as:
Lsihoouttte Render Render lg
(6)
Subsequently, we optimize the distortion of the predicted UV map by adding a distortion loss term. Specifically, for each triangular facef, we compute the Jacobian matrix of its 3D-to-2D mapping, and after performing Singular Value Decomposition, we obtain the singular values o and o², which characterize the stretching intensity. The distortion loss function de- fined as the mean of the absolute differences of the singular values across all faces, which ap- proaches zero in the case of ideal conformal mapping.
Hendergred
Handerge
Figure 5: UV map silhouette.
Σ FEF
14 - -o
Ldistortion
ΣFEF Ay
where F is the set of all triangles on the input surface and |Ay| is the area of f.
Finally, prevent UV map overlaps that would adversely affect subsequent texture mapping pro- cesses, we implement an overlap penalty based on normal direction. The key observation is that overlapping triangular faces the UV domain exhibit flipped normal directions. We formulate this constraint by introducing penalty term proportional to the count of faces with negative normal directions:
Loverlap
(ny - 0)
FEF
where F represents faces in the UV map, ny is the normal vector of face f in the UV map, and Z is the reference viewing direction.

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Published as a conference paper at ICLR 2026
Consequently, The complete objective function combines these loss terms through a weighted linear combination:
Luotal
Wi Trecon wis Lsilhouette + wi -distortion wo Loverlap
where wa, (i = d, o) represents the weight for each corresponding loss component.
EXPERIMENT
We conduct both qualitative and quantitative evaluations of our model the ArtUV-200K and the FAM benchmark. First, we compare the UV unwrapping performance of our parameterization model with that of mainstream professional modeling software. Next, we compare our complete Ar- IUV method with some advanced algorithmss covering the entire process from surface segmentation UV parameterization. Finally, we perform ablation studies assess the rationale and effective- ness of our loss function design from multiple perspectives.
5.1 IMPLEMENTATION DETAILS
Gea We consulted the authors of SeamGPT for detailed dataset and model implementation specifies, and successfully reproduced the complete SeamGPT model. For additional details, please refer to Supplementary Materials.
ArtUV parameterization. We use Blender's ministretch algorithm as the initialization method for the UV parameterization module. The Res-M MLP module map the feature dimensions of the input UV, vertices, normals, curve, and degree 128, 62, 32, 32, and 32. These features then passed through 5 SageConv layers, resulting in a 512 dimensional graph feature. The extracted features are processed by an attention encoder with 512 dimensions, heads, and 8 layers. The coarse-to- fine decoder then nown-samples the features by factors of 1/2 and 1/4. Finally, the output mapping layer predicts the UV coordinates (dimensionality 2) using a Tanh activation function, ensuring the predictions remain within the range [-1,1] 1]. The model is trained on 24 H20 GPUs (96 GB) for 700K steps with a batch size 32. the loss function, the weights wrs Was Wa and W, are set as 1.0, 1.0, 0.0001 and 0.01. Inference can be performed most consumer-gras grade GPUs, with memory usage not exceeding 10 GB when the model contains fewer than 1000 faces.
5.2 COMPARED WITH PROFESSIONAL SOFTWARE
We first compare our UV pa- rameterization model with cur- rent professional modeling soft- ware in the AnUV--20K, which contains 100 diverse 3D mod- els, each with artist-manually marked seams. This allows us to eliminate any interference from seams quality and directly com- pare the unwrapping results. We conduct quantitative evaluations in terms of mesh distortion and UV utilization. Specifically, the mesh distortion is computed as the average conformal energy over all triangular faces of the mesh. For UV utilization, we apply the UVPackMaster plugin for layout optimization in all unwrapping algorithms.
Method
Distortion
Utilization (%6)
Artist-Levelf
Blender
9.85
62.74
3.34
Maya
9.66
67.53
1.32
3DsMax
11.88
67.01
1.53
Artist-manual
10.90
70.08
4.12
Ours
9.52
72.57
4.22
Table 1: Quantitative results on AnUV-200K benchmark.
The metrics are presented in Table D where our algorithm outperforms in all evaluated criteria. Compared to professional modeling software, our algorithm significantly improves UV utilization while maintaining low distortion. Even when compared to mrtist-manually unwrapping results, our approach exhibits better performance in both distortion and utilization, indicating that our algorithm achieves a level of perfect balance between distortion and neatness that difficult for conventional algorithms manual to attain. Furthermore, using the same texture reference, apply the texture generation method (Zhao et al. (205)) to UV maps produced by different approaches. The visual- izations in Figure 6 show that unwrapping method produces Horizontal and vertical UV
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13.13
71 .36%
71. 10%
16.70%
a2 60%
77 74.6
76, 75
70.84%
(a] Input
(b)
[c]
[d] Maya
(c) 3DsMax
Figure 6: Qualitative Result on AnUV-200K. (a) The input mesh and reference texture image. (b)- (e) Results of our method, along with Blender, Maya, and 3DsMax. For each method, the upper-left image shows the UV unwrapping visualized using normal directions, the lower-left image displays the texture map based the reference texture, with the value below the texture map representing the UV utilization rate, and the right image presents the 3D model with checkerboard texture.
maps. This not only leads a substantial increase in UV utilization, but also makes the texture map clearer, providing significant convenience for subsequent tasks such as texture editing.
Moreover, because the quality of artistic style is inherently subjective, we conducted user study to further evaluate the Artist-Level of our results. We randomly selected 10 representative cases from the ArtUV-200K and invited 30 professional 3D artists to score the artistic style the generated UV maps five-point Likert scale, where 5 indicates results most similar artist-created UV maps and represents no resemblance to artistic style. As shown in the last column of Table [ our method even slightly surpass those produced by manual artist adjustments, demonstrating its strong capability to capture the intended artistic style in UV unwrapping.
5.3 COMPARED WITH SOT ALGORITHM
We then evaluate fully automated ArUV method using the FAM benchmark (which lacks seam information), comparing it with three algorithms: XAtlas, Nuvo, and FAM. The comparison covers the entire UV wrapping process, including surface segmen- tation and UV parameterization. Quantitative evaluations in Table 2 based on mesh distor- tion, computational runtime and UV islands count demonstrated that our algorithm out- performed the others. is evident that XAtlas produces overly fragmented and semantically poor segments, while Nuvo and FAM suffered from prolonged computation times due to requiring per- training. Furthermore, as shown in Fig- ure [ the UV unwrapping results from Nuvo and FAM, characterized by disorganized topology, are impractical for professional rendering pipelines, making a comparison of UV utilization unnec- essary well. In contrast, ArtUV method demonstrates outstanding performance, providing
Method
Distortion
Runtime(s)
Fragments
XAtlas
9.44
80.4
1292
Nuvo
32.24
2925.8
FAM
76.28
5656.3
1
Ours
8.91
36
14
Table 2: Quantitative results on FAM benchmark.
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Spot
Arm
Homer
(+) Input
(b) Ours
(c) Xatlas
(d) Nuvo
(6) FAM
Figure 7: Qualitative Result FAM (Snoh Arm, and Homer). (a) shows the input mesh from the FAM benchmark, while (b) to (e) present the UV unwrapping results for our method, XAtlas, Nuvo, and FAM, respectively. It is clear that our algorithm directly produces elegant and high-quality UV maps. In contrast, XAtlas results in overly fragmented and semantically weak UV maps, while Nuvo and FAM generate disordered and unusable UV maps.
an end-to-end solution that directly generates UV maps with low distortion, organized layout, and semantic information, fully meeting the design standards of artists.
5.4 ABLATION STUDIES
We conducted several ablation stud- ies evaluate the contributions of our designed loss functions.
Silhouette Loss. As shown in Fig-
ure [8 (a) and Table 3 when the sil- houette loss is omitted, the model fails to optimize the boundaries, re- sulting less aligned and struc- tured UV maps, which in turn leads lower UV utilization and reduced Artist-Level scores.
swertap
wyjo distortion loss
detortion lods
Overlap Loss. Including the overlap loss suppresses the model's tendency to generate overlapping UV faces. As shown in Figure (a), the UV maps are more orderly with this loss. Ta- ble further shows significant reduction in overlapping face when the overlap loss is applied.
Figure 8: Qualitative ablation results.
Distortion Loss. Figure (b) visualizes UV distortion using color-coded scheme, where brighter yellow regions indicate higher distortion. Quantitative results in Table demonsddeosttttaee that adding the distortion loss adjusts internal UV coordinates to more reasonable positions, effectively reducing distortion.
CONCLUSION
In this paper, propose ArtUV, end-to-end method for generating artist-style UV maps. We de- compose the problem into surface segmentation using SeamGPT and UV parameterization that pre- diets offsets from initial UV maps artist-style results. Our method generates neat, well-organized

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Loss
Distortion
+
Loss
Overlap (%) 4 Loss
Utilization (%)
Artist-Level f
w/o Dist.
10.56
w/o Ovlp.
29.0
w/o Sil.
64.33
3.67
w/ Dist.
9.52
w/ Ovlp.
0.0
w/ Sil.
72.57
4.12
Table 3: Quantitative ablation results.
UV maps with low distortion seconds, addressing current issues long processing times and lack of semanticity in professional workflows. Extensive experiments demonstrate that ArtUV out- performs existing approaches across multiple metrics, holding significant potential for efficiency improvements in downstream applications.
Limitation. Our current approach exhibits two key limitations. First, the method's performance is highly sensitive to the quality of surface cutting. Incomplete inaccurate seams may cause severe distortions during UV initialization, resulting in significant internal deformation despite the output maintaining clean edges. Second, our pipeline does not yet support UV island reuse, since imperfect alignment of reused islands might cause serious overlapping artifacts. Additionally. island reuse may introduce additional complexity model training. Future work will focus on: (1) enhancing the quality and stability of seams (e.g., applying secondary segmentation to high-distortion areas), and (2) integrating UV island reuse the pipeline (e.g., via similarity- merging optimized islands).
REFERENCES
Dena Bazazian and M Eulalia Parés. Edc-net: Edge detection capsule network for 3d point clouds. Applied Sciences, 11(4):1833, 2021.
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. Objaverse: A universe of anno- tated 3d objects. arXiv preprint arXiv:2212.08055 2022.
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, Eli VanderBilt, Aniruddha Kembhavi, Carl Vondrick, Georgia Gkioxari, Kiana Ehsani, Ludwig Schmidt, and Ali Farhadi. Objaverse-xl: A universe of 10m+ 3d objects. arXiv preprint arXiv:2307.0560 .05663,, 2023.
Michael S Floater and Kai Hormann. Surface parameterization: a tutorial and survey. Advances in multiresolution for geometric modelling, pp. 157-186. 2005.
Huan Fu, Rongfei Jia, Lin Gao, Mingming Gong, Binqiang Zhao. Steve Maybank, and Dacheng Tao. 3d-future: 3d furniture shape with texture. International Journal of Computer Vision, pp. 1-25, 2021.
Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in neural information processing systems, 30, 2017.
Zekun Hao, David W Romero, Tsung-Yi Yi Lin, and Ming- Yu Liu. Meshtron: 3d mesh generation at scale. arXiv preprint (D:::24/..09545 2024.
artist-like
Chems-Eddine Himeur, Thibault Lejemble, Thomas Pellegrini, Mathias Paulin, Loic Barthe, and Nicolas Mellado. Pcednet: A lightweight neural network for fast and interactive edge detection in 3d point clouds. ACM Transactions on Graphics (TOG), 41(1):1-21, 2021.
Berthold KP Horn Closed-form solution of absolute orientation using unit quaternions. Journal of the optical society of America A, 4(4):629 642, 1987.
Zhongshi Jiang, Scott Schaefer, and Daniele Panozzo. Simplicial complex augmentation framework for bijective maps. ACM Transactions on Graphics, 36(6), 2017.
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, Arxiy, 2023.
10

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Published as a conference paper at ICLR 2026
Minchen Li, Danny M Kaufman, Vladimir G Kim, Justin Solomon, and Alla Sheffer. Optcuts: Joint optimization of surface cuts and parameterization. ACM transactions on graphics (TOG), 37(6):
1-13, 2018.
Yang Li, Victor Cheung, Xinhai Liu, Yuguang Chen, Zhongjin Luo, Biwen Lei, Haohan Weng. Zibo Zhao, Jingwei Huang, Zhuo Chen, et al. Auto-regressive surface cutting, arXiv preprint arXiv:2506.18017, 2025.
Helmut Lütkepohl. Vector autoregressive models. In Handbook of research methods and applica- Edward Elgar Publishing, 2013.
fions in empirical macroeconomics, pp.
Michael Rabinovich, Roi Poranne, Daniele Panozzo, and Olga Sorkine- Hornung. Scalable locally injective mappings. ACM Transactions on Graphics (TOG), 36(4):1, 2017. 2017.
Umme Sara, Morium Akter, and Mohammad Shorif Uddin. Image quality assessment through fsim, ssim, mse and psnr- a comparative study. Journal of Computer and Communications, 7(3):8-18, 2019.
Alla Sheffer, Bruno Lévy, Maxim Mogilnitsky, and Alexander Bogomyakov. Abf++: fast and robust angle based flattening. ACM Transactions on Graphics (TOG), 24(2):311 2005.
Alla Sheffer, Emil Praun, Kenneth Rose, et al. Mesh parameterizzaiion methods and their applica- tions. Foundations and Trends in Computer Graphics and Vision, 105) 2007.
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 IEEE Visualization, 2002. VIS 2002., pp. 355-362. IEEE, 2002.
Pratul P Srinivasan, Stephan J Garbin, Dor Verbin, Jonathan T Barron, and Ben Mildenhall. Nuvo: Neural uv mapping for unruly 3d representations. European Conference on Computer Vision, pp. 18-34. Springer, 2024.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, łukasz Kaiser, and Illia Polosukhin. Attention all you need. Advances in neural informa- tion processing systems, 30, 2017.
Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amirt and Hao Zhang. Pie-net: Parametric inference of point cloud edges. Advances in neural information processing systems, 33:20167-20178, 2020.
Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Orh and Pheng-Am Heng. Ec-net: an edge- aware point consolidation network. In Proceedings of the European conference on computer vision (ECCV), pp. 302,402, 2018.
Qijian Zhang, Junhui Hou, Wenping Wang, and Ying He. Flatten anything: unsupervised neural surface parameterization. Advances in Neural Information Processing Systems, 37:2830-28500
2024.
Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang, Sheng Zhang, Xianghui Yang, et al. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation. arXiv preprint arXiv:2501. 12202, 2025.
A APPENDIX
A.1 MORE RESULTS
A.1.1 QUANTITATIVE RESULTS ON FAM BENCHMARK.
As shown in Table we provide a detailed comparison of our method with XAtlas, Nuvo, and FAM in terms of the distortion metrics for each category in the FAM-benchmark.
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Method
XAtlas
Nuvo
FAM
Ours
Bimba
15.44
19.12
12.10
20.02
Lucy
0.011
57.894
35.13
0.043
Ogre
0.66
26.22
11.55
0.75
Armadillo
0.17
114.21
59.87
0.3492
Bunny
61.83
16.84
7.33
58.19
Nefertiti
0.026
20.92
11.2
0.23
Dragon
0.22
61.02
904.89
0.12
Planck
0.14
11.09
4.67
0.062
Homer
7.51
21.92
14.19
19.20
Teapot
2.42
17.56
8.77
3.06
Cheburashka
8.41
19.75
12.21
10.86
Spot
12.77
12.93
9.37
8.73
Arm
29.98
37.34
20.98
8.54
Beast
0.062
34.19
23.54
1.38
Cow
1.94
12.70
8.49
1.52
Avg. 9.44
32.24
76.28
8.91
Table 4: Quantitative results on FAM benchmark using the face distortion metric.
A.1.2 QUALITATIVE RESULT ON ARTUV-200K BENCHMARK.
present more UV unwrapping results from the ArtUV-200K bench- mark. It is evident that the maps obtained using our method are more elegant and well-organized.
As shown in the Figure 9
A.1.3 RESULTS OF DIFFERENT INITIALIZATION METHODS.
Our AnUV parameterization module can be seamlessly integrated as a plugin with various pro- fessional modeling software. To demonstrate its robustness, we replaced Blender's initial UV un- wrapping results with Maya and 3DsMax based initialization. As shown in Figure TO our method consistently produces high-quality, artist-style UV maps regardless of the initialization approach used.
A.2 MORE IMPLEMENTION DETAILS OF SEAMGPT
We consulted the authors of SeamGPT for detailed dataset and model implementation specifics, and successfully reproduced the complete SeamGPT model. Begin with a targeted point sampling strategy that collects a total of 61,440 points-evenly, split between 30.720 points on vertices and 30,720 points along edges. Then, we implement hierarchical hourglass-stre
decoder defined by three-level abstraction structure with depth configuration (2, (4, 12, 4). 2), where each number represents the number of transformer blocks at that level. Each block has 1,536 dimensions and 16 attention heads, incorporating 10-bit quantized positional encoding for sequences up to 36,864 tokens. The model is trained on 64 H20 GPUs(96GB) for 200k steps with fixed learning rate of Ic-4, gradient clipping at 0.5 and batch size 128. Data augmentation techniques including random scaling with (0.95, 1.05), random vertex jitter with noise level 0.01, and random rotation implemented to improve model robustness during the training process.
sequenccs
A.3 CODE
We provide our demo inference and model code as part of our supplementary materials. All source code and pretrained models will be made publicly available upon acceptance.
A.4 DATASET
AntUV-200K benchmark is tentatively planned for public release upon paper acceptance. At the same time, will release data processing seripts including UV island segmentation and data filtering to assist researchers in curating high-quality UV data from proprietary datasets.
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O0
(a) Input
(b) Ours
(c) Blender
Maya
(e) 3DsMax
Figure 9: More Qualitative Results on ArUVV-20K.
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(a) Input
(b) Maya based
(c) 3DsMax based
Figure 10: Results of different initialization methods.(a) Input original mesh; (b) and (c) display unwrapping results based on Maya initialization and 3D&Max initialization, respectively. For each method, the left sub-figure shows the initial unwrapping result, while the right sub-figure presents the optimized output after processing through our ArUV parameterization module.
14


Reviews:
[{"review_id": "YoFRrkuQRF", "reviewer": "Reviewer_Wp8T", "summary": "The paper proposes ArtUV, a novel method for UV unwrapping that aims to generate artist-style UV maps. UV unwrapping is critical for tasks such as texture mapping and lightmap generation. The proposed method divides the process into two stages: surface seam prediction using SeamGPT, and UV parameterization using a specialized Auto-Encoder. ArtUV is designed to address the shortcomings of existing methods, such as fragmentation, lack of semantic coherence, and high computational demands.", "questions": "- How does ArtUV perform when applied to non-standard or highly irregular meshes (e.g., organic models with non-manifold edges)? Are there specific types of models or topologies where the method might fail?\n\n- While the method shows good results in benchmarks, how well does it scale when integrated into large-scale production pipelines with diverse and complex 3D assets? Could performance degrade with larger models or meshes with higher vertex counts?\n\n- Given that SeamGPT’s accuracy is critical for ArtUV’s performance, how robust is it to small errors in seam prediction? Could slight inaccuracies in cutting lead to major issues in the final UV map, especially in intricate areas like facial features or other highly detailed parts?\n\n- How does the method handle texture mapping for more complex or varied textures (e.g., procedural textures or dynamically generated ones)? Does it maintain its effectiveness for non-static textures?\n\n- Please provide more justifications on how the proposed method generalizes to in-the-wild cases. This would help assess its ability to generalize across various 3D assets.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- Structure looks interesting. ArtUV introduces a hybrid approach by combining SeamGPT for surface seam prediction and an Auto-Encoder for UV parameterization. This combination seems promising for addressing both the technical and artistic challenges of UV unwrapping.\n\n- The method demonstrates significant improvements in UV utilization and distortion minimization when compared to professional software such as Blender, Maya, and 3DsMax, as shown in the experiments and visual results.\n\n- The paper provides extensive experimental validation using both qualitative and quantitative measures, demonstrating the superiority of the method in terms of distortion, utilization, and runtime across various benchmarks", "weaknesses": "- The proposed method seems to work only on manifold meshes. However, artist-style meshes often go with arbitrary design patterns. The method may not generalize well to a wide range of 3D object types, particularly those that require non-traditional unwrapping methods.\n\n- While SeamGPT provides semantic cutting predictions, it requires careful training and may struggle with particularly challenging meshes that deviate from typical models. The method could potentially fail for meshes that have unusual topologies or textures.\n\n- The method is highly sensitive to the initial quality of surface cutting (seam prediction). Inaccurate or incomplete seams can lead to significant distortions during the UV initialization process, which may degrade the final result, especially for complex models", "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": "z6gHoVRKrJ", "reviewer": "Reviewer_8ZrW", "summary": "This paper studies UV map refinement. The authors introduce a neural network (ArtUV) that learns to predict per-vertex offsets of an UV map using a weighted multi-term loss function. By adding the predicted offsets to the vertices, it moves the islands of an UV map into an artist style layout. The main strength of this paper is that it addresses a valuable practical problem in the 3D graphics industry. The proposed method makes a UV map easier for artists to manipulate and increases space utilization.The main weaknesses are 1) lack of academic novelty and 2) over-claimed contributions. Overall, I would rate this paper as Weak Accept (WA). Although its contribution lies in system-level innovation rather than academic novelty, it provides a practical solution for industry applications.", "questions": "1.Does UV map’s resolution affect performance? Please find three AI-GENERATED 3D models with simple, normal, complex geometry structure, and run your pipeline using UV resolutions of 256×256, 512×512, and 1024×1024.\n\n2.Please investigate if ArtUV improves rendering quality.  Please find three AI-GENERATED 3D models with simple, normal, complex geometry structures, render them using Init Map and ArtUV’s output. Check if the rendered images’ quality is improved by ArtUV.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.This paper is clearly written.\n\n2.The proposed approach is valuable for applications.", "weaknesses": "1.Overstated contribution. The paper claims to present an end-to-end system, yet key components—SeamGPT for seam prediction and Ministretch-Unwrap for initialization—are borrowed from prior works. In fact, UV map refinement is a valuable problem in its own right; \n\n2.There is no need to bring SeamGPT here merely to inflate the contribution. Instead, provide an analysis of the core challenges in UV-map refinement, and explain how your network design (e.g., multiple inputs, SAGEConv, etc.) addresses each, would clarify the true contribution and strengthen the paper.", "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": "yFri52Ui3u", "reviewer": "Reviewer_Erhu", "summary": "The paper presents a method to automatically perform UV unwrapping combining multiple state of the art methods. In particular, they use SeamGPT to infer cutting seams and then use them to run an optimisation procedure aimed at providing an initial UV map. Initial UV map and mesh info are then processed by a neural network using adaptive dimension mapping to modulate the importance of the different inputs (Res-M MLP), graph convolutions to locally fuse information (SAGE Conv) and an attention-based module (Pyramid ED) to faclitate global communication. This paper also introduces a curation of high-quality textured objects sourced from existing mesh datasets.", "questions": "After reviewing the paper I have several outstanding doubts and questions, which are listed below alongside a list of suggestions to improve the paper:\n\n\n\n1\\. I believe more context is needed in the introduction, especially for a venue whose main focus is not Graphics or 3D vision. Readers and participants are not necessarily familiar with the concept of UV mapping. I would suggest providing more details on what textures are and what they are used for. It would be also more comprehensive if the authors could mention methods that aim to skip this step and represent textures directly on the surface of objects (e.g., i, ii, iii, iv).\n\n\n\n2\\. Why should larger islands destabilise training?\n\n\n\n3\\. At line 161 authors claim that they manually selected UV islands. Were authors able to manually inspect 200k islands? Reading lines 162-163 I am under the impression that the selection was not manual, but mostly automated. \n\n\n\n4\\. By using ministretch to assess the quality of UV textures during data curation, authors implicitly assume that this automatic procedure is capable of producing satisfactory UV maps. How can a presumably flawed method be used as rejection criteria?\n\n\n\n5\\. Authors state that \"extensive empirical evidence\" proved that directly learning the mapping from 3D is challenging (line 215). Where is this evidence? Please, cite relevant literature or report personal \"extensive evidence\".\n\n\n\n6\\. At line 217 authors state that a simple projection is not their goal and imply that this is a consequence of choosing meshes as input. I suppose that this was not the intended claim and I would encourage them to restructure the sentence as input meshes don't automatically translate into simple projections. \n\n\n\n7\\. Please adopt a more rigorous notation. For instance:\n\n&nbsp;  - line 221, I\\_M is a set, as such it would be more appropriate to define the following: I\\_M = {V, F, ...D}, I = I\\_m U Q\\_i. \n\n&nbsp;  - \\\\bar{p} and \\\\bar{q} were never formally defined. \n\n&nbsp;  - notation in Eq. 5 is inconsistent with Fig. 4\n\n&nbsp;  - the rotation matrix computed in Eq. 4 should probably be part of Eq. 5 and 6\n\n\n\n8\\. Are Q and V sharing the same faces? Cutting seams should introduce vertex duplications to unwrap the surface. It is unclear to me which topological information is used and fed to the networks. Also, how is Res-M MLP processing faces and edges?\n\n\n\n9\\. Which specific technique was used for adaptive dimension mapping in the Res-M MLP? Briefly describe how it works. \n\n\n\n10\\. Comparisons against Blender, Maya and 3DsMax are conceptually reasonable, but a better understanding of the underlying techniques is necessary. What these software implement today may differ from what they are going to use tomorrow. In addition, results for Maya and 3DsMax are rather similar. The main difference appears to be how islands are placed in the image rather than their shapes. It is very likely that they implement the same underlying method considering that they are both produced by Autodesk. The different island placement could be just a matter of some randomness in their procedure or hyperparameter choice.\n\n\n\n11\\. Also the following would benefit from additional details in terms of the inner workings and ideally be introduced with a one/two plain English sentences describing how they operate:\n\n&nbsp;  - Blender ministretch (first occurrence at line 161), \n\n&nbsp;  - SSIM (line 162)\n\n&nbsp;  - Conformal energy (line 376), please provide more details, there is a very extensive literature on conformal mapping.\n\n&nbsp;  - UVPackMaster plugin (line 377)\n\n\n\n12\\. XAtlas appears to achieve lower distortion results in the qualitative results depicted in Fig. 7. It would be great to see more examples and better understand the distortion metric used to determine whether or not it can be considered reliable. \n\n\n\n\n\nPlease note that given the weaknesses highlighted in the weaknesses section I am unlikely to increase my score above the acceptance threshold. I believe that too many details were omitted in the initial submission and even a good rebuttal may not be sufficient to ensure all the problems are properly addressed. Limited technical novelty and poor justification of methodological choices are my second major concern.\n\n\n\n\n\nReferences:\n\n\\[i] Yu, X., Dai, P., Li, W., Ma, L., Liu, Z., \\& Qi, X. (2023). Texture generation on 3d meshes with point-uv diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4206-4216).\n\n\\[iii\\\\] Wei, J., Wang, H., Feng, J., Lin, G., \\& Yap, K. H. (2023). Taps3d: Text-guided 3d textured shape generation from pseudo supervision. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 16805-16815).\n\n\\[iii] Foti, S., Zafeiriou, S., \\& Birdal, T. (2024). Uv-free texture generation with denoising and geodesic heat diffusion. Advances in Neural Information Processing Systems, 37, 128053-128081.\n\n\\[iv] Oechsle, M., Mescheder, L., Niemeyer, M., Strauss, T., \\& Geiger, A. (2019). Texture fields: Learning texture representations in function space. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4531-4540).", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The paper presents an automated method for UV unwrapping which appears to surpass many state of the art methods. The pre-trained model and dataset could be of value for the community as well as for 3D artists. The paper is grammatically well written, but not entirely scientifically sound (see Weaknesses and Questions).", "weaknesses": "\\- As later detailed, many citations are missing. Authors make extensive use of graphics software (e.g. Blender, Maya, 3DsMax), but fail to investigate the underlying techniques. This confers the paper an overall tone of superficiality and limited knowledge of the field. \n\n\\- Technical novelty seems to be limited for the venue or authors fail to successfully highlight their core innovation. The main contribution appears to be the arbitrary combination of existing methods. While this could be an acceptable contribution the choice of the different \"building-blocks\" appears arbitrary. For instance, there is no written nor proven justification as to why SAGE Conv or Pyramid ED should be preferable to the many potential alternatives. An ablation of the model components and some benchmarks against potential alternatives would have been appreciated.\n\n\\- Mathematical notation is inconsistent and not rigorous. \n\nSome of these are further detailed in the questions section.", "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": "nf7vFziT6i", "reviewer": "Reviewer_ZizJ", "summary": "The authors address the challenge of artist-level UV unwrapping. To achieve this, they begin by reimplementing SeamGPT to partition the mesh into distinct, semantically meaningful islands. An initial UV unwrapping is performed using the Ministretch unwrap method. However, the results of this step often exhibit distortions in UV space. Skilled artists carefully arrange UVs “neater”. To address this, they propose learning offsets that distort the initial unwrapping to suit the preferences of artists. The process starts with the embedding of features on each vertex and the subsequent execution of a graph neural network. Subsequently, a PyramidED network is employed to enable global vertex interaction. The outcome is then 2D offsets that are applied to the initial unwrapping. Two novel losses are introduced for training this offset generation. Lastly, the individual islands are consolidated into a single atlas using a pre-existing method.", "questions": "- Why are Nuvo and FAM run in single chart mode? At least performing the Multi-Chart mode would result in slightly better results. Albeit not artist level.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- The dataset release, along with the accompanying processing scripts, are highly valuable for future research.\n- The introduction of the paper is well-written, particularly the section on the various approaches to UV unwrapping.\n- Overall, the paper’s structure is clear and easy to follow.\n- The two novel losses are well explained and motivated. They are also appropriately evaluated and fulfill their intended objectives as shown in ablations.\n- The results presented are impressive, especially considering that the Artist-Level rating is surpassed by ArtUV even when compared to manual UV unwrapping.\n- ArtUV has also a rather short run-time, particularly when applied to lower poly meshes. As mentioned in the Weakness section, SeamGPT may pose a potential limitation. However, the method remains applicable if more efficient cutting networks are discovered.", "weaknesses": "- Some weaknesses of the method are inherited from SeamGPT. Especially, that it requires significant amounts of memory and according to the original authors has a limit of roughly 20k vertices.", "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)