QuadGPT: Native Quadrilateral Mesh Generation with Autoregressive Models
Authors: Jian Liu
Chunshi Wang
Song Guo
Haohan Weng
Zhen Zhou
Zhiqi Li
Jiaao Yu
Yiling Zhu
Jing Xu
Biwen Lei
Zhuo Chen
Chunchao Guo
Venue: ICLR 2026 Poster (2026)
Keywords: Autoregressive Quad Mesh Generation
Reinforcement Learning
Topology Optimization
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A novel method that directly generates quad-dominant meshes with superior topology, overcoming the limitations of conversion-based approaches.

Abstract: The generation of quadrilateral-dominant meshes is a cornerstone of professional 3D content creation. 
However, existing generative models generate quad meshes by first generating triangle meshes and then merging triangles into quadrilaterals with some specific rules, which typically produces quad meshes with poor topology.
In this paper, we introduce QuadGPT, the first autoregressive framework for generating quadrilateral meshes in an end-to-end manner. 
QuadGPT formulates this as a sequence prediction paradigm, distinguished by two key innovations: a unified tokenization method to handle mixed topologies of triangles and quadrilaterals, and a specialized Reinforcement Learning fine-tuning method tDPO for better generation quality. 
Extensive experiments demonstrate that QuadGPT significantly surpasses previous triangle-to-quad conversion pipelines in both geometric accuracy and topological quality. 
Our work establishes a new benchmark for native quad-mesh generation and showcases the power of combining large-scale autoregressive models with topology-aware RL refinement for creating structured 3D assets.

Published as a conference paper at ICLR 2026
QUADGPT: NATIVE QUADRILATERAL MESH GENER- ATION WITH AUTOREGRESSIVE MODELS
Jian Liu
Chunshi Wang
Song Guo
Haoban Weng
Zhen Zhou
Zhiqi Li
Jiaao Yu²
Yiling Zhu
Jing Xu²
Biwen Lei
Zhuo Chen Chunchao Guo
Hong Kong University of Science and Technology.
Tencent Hunyuan.
Figure 1: QuadGPT can generate diverse, quad meshes conditioned on point clouds.
ABSTRACT
The generation of quadrilateral-dominan meshes is cornerstone of professional 3D content creation. However, existing generative models generate quad meshes by first generating triangle meshes and then merging triangles into quadrilaterals with some specific rules, which typically produces quad meshes with poor topol- ogy. In this paper, we introduce QuadGPT, the first autoregressive framework for generating quadrilateral meshes in an end-to-end manner. QuadGPT formulates this as a sequence prediction paradigm, distinguished by two key innovations: a unified tokenization method to handle mixed topologies of triangles and quadrilat- erals, and a specialized Reinforcement Learning fine-tuning method tDPO for bet- ter generation quality. Extensive experiments demonstrate that QuadGPT signif- icantly surpasses previous triangle-to-quad conversion pipelines in both geomet- ric accuracy and topological quality. Our work establishes new benchmark for native quad-mesh generation and showcases the power of combining large-scale autoregressive models with topology-awe aware RL refinement for creating structured 3D assets.
INTRODUCTION
In the creation of high-fidelit 3D assets for game development, quadrilateral-oominee meshes play a fundamental role in ensuring modeling efficiency, deformation stability, and animation readiness. The structured topology provided by quad-dominant layouts facilitates smoother subdivision sur- faces, more natural articulation, and easier UV unwrapping, all of which are essential production
"Corresponding authors.

---

Published as a conference paper at ICLR 2026
Marching Cobes:Simpliiication
Trimeeh Generation
Trimeeh Tri2Qund
QuaGGPT (Ours)
Figure 2: Comparison of topological quality across different pipelines. Iso-surfacing meth- ods produce dense, unstructured triangular meshes. Autoregressive triangle generation followed by heuristic conversion fails to create coherent structure. Our QuadGPT directly generates native quadrilateral meshes with clean, artist-friendly edge flow.
environments (Lei et al., 2025). Consequently, quad meshes have become the industry standard, especially in character and organic modeling, where clean edge flow and controllable curvature are critical.
In the pursuit of automated generation of artist-ready 3D assets from inputs such as text or images, existing approaches typically decouple the problem into separate stages: geometry generation and topology generation. For geometry, latent diffusion models (LDMs) have achieved remarkable success in generating 3D shapes (Zhao al. 2025b Li al.) 2025d) via implicit representations
like SDF (Li et al. 2024). Meshes are then extracted with iso-surface algorithms (Lorensen & Cline 1998 Shen al. 2021]2003 inevitably resulting in unstructured dense meshes. On the other hand, fopcled methods using cross-field guidance (Huang et al. 2018 Dong et al. (225a)) aim for structure but are often not robust, requiring pristine input meshes and failing produce the adaptive, artist-like tessellation where polygon density matches geometric complexity.
More recently, autoregressive mesh generation methods such as MeshAnything (Chen et al. 2025b), BPT (Weng et al. (025)) and Mesh-RFT (Liu al. 2025) have shown promise by modeling mesh sequences with Transtormer architectures. While these approaches capture artist-like topology, they remain limited to generating only triangular meshes. Converting these outputs into quadrilateral meshes still requires rrangleemerging algorithms that often break natural edge flow and introduce artifacts, demonstrated in Figure 2 Consequently, even high-quality triangle meshes hard to translate into production-ready quad layouts, highlighting a fundamental discrepancy between generated 3D assets and industrial applications.
To address these challenges, we propose an end-to-end autoregressive framework for direct genera- tion of native quadrilateral meshes. Our model consumes point cloud as input and produces struc- tured face sequence as output. Recognizing that artist-crafted meshes are typically quad-dominant yet usually incorporate a small number of triangles, design a novel unified representation that ex- plicitly supports mixed-element topologies through a tailored padding strategy for triangular faces. For computation efficiency, we employ an Hourglass Transformer architecture that first condenses the face sequence and subsequently compresses vertex information. The model is trained using truncated sequence strategy, enabling support for high-poly meshes. To further improve topology quality, we introduce a reinforcement learning fine-tuning phase with truncated direct preference optimization (tDPO) that rewards the formation of coherent edge loops, a characteristic feature of professionally designed assets. Our reinforcement learning framework is specifically designed to evaluate and compare truncated sequences, ensuring effective optimization even for large-scale meshes.
2

---

Published as a conference paper at ICLR 2026
Extensive experiments the Toys4K dataset (Stojanov et al. 2021 confirm that QuadGPT con- sistently generates higher-quality 3D meshes than tfate-of-the-ar baselines. By leveraging a large- scale, carefully curated dataset and comprchensive pre-training and post-training protocols, we fur- ther evaluate QuadGPT on dense meshes generated by Hunyuan3I (Zhao et al., 2025b). The model demonstrates robust performance across both soft-surface models (e.g., human characters) and hard- surface objects (e.g., props). To ensure a rigorous comparison, we trained an triangle-only variant (TriGPT) followed by triangle-to-quad conversion. As shown in Figure QuadGPT's native quadri- lateral architecture yields significantly superior topology. This breakthrough establishes QuadGPT as an unequivocally state-of-the -h-the-rrtt solution, effectively bridging the gap between text/image inputs and production-ready 3D artist meshes.
The main contributions of this paper are summarized as follows:
We present QuadGPT, the first autoregressive model that generates native quad-dominant meshes in an end-to-end manner.
We propose unified sequence representation for mixed-element meshes with a padding- serialization, enabling the scalable process of heterogeneous mesh topologies.
We introduce tDPO, which designed to optimize global quadrilateral flow through a novel re- ward mechanism that encourages the formation of structured edge loops.
QuadGPT achieves state-of-the-an performance, producing game-ready meshes that exceed exist- ing methods in both geometric and topological quality.
2 RELATED WORK
Indirect and Field-Guided Mesh Generation. A dominant paradigm in 3D generation relies on continuous neural representations like vecsets (Zhang al. 2023 2024 Li et al. 2024 Zhao al. 2023 Wu et al. 2024 Li et al., 2025b; Zhao et al. 20256) or sparse voxels (Xiang et al. 2024 Ye et al. 2025 He al. 2025 Wu al. 2025 Li et al. 2025d). A universal limitation of these methods is their reliance on an iso-surfacing step, such as Marching Cubes (Lorensen & Cline 1998). which invariably yields dense, topologically unstructured triangular meshes. Concurrently, traditional approaches to quadrilateral meshing are predominantly guided by cross-field computa-
tion. These methods are either optimization-bun based, requiring slow, per-shape optimization (Bommes
et al. 2009 Jakob et al. [2015] Huang et al.] 2018 Bommes et al.) 2013: Ebke et al.) [016 Campen & Kobbelt 2014] Jiang et al. 2015| Diamanti al. 2015), or more recent learning based techniques
that accelerate field prediction (Dielen et al.) [021 Li et al. 2025c Dong et al. 2025b a). However, all field-guided methods depend on multi-stage pipelines that are not end-to-end generative frame-
works.
Native Triangle Mesh Generation. To address the limitations of indirect methods, a promising direction has been the direct autoregressive generation of mesh sequences. This field, pioneered by
MeshGPT (Siddiqui et al.] 2024), has seen rapid progress. Subsequent work has largely focused
on three key areas: (1) developing more efficient tokenization and compression schemes manage
long sequences (Chen et al., 2024 2025c Tang et al. 2025 Weng et al. 2025 Lionar et al. 2025
Song et al., 2025 Kim et al. 2025 (2) achieving scalability tens of thousands of faces through
architectural innovations, most notably with the Hourglass Transformer in Meshtron (Hao et al.) 2024]; and (3) enhancing output quality and alignment with human preferences via reinforcement learning, demonstrated by DeepMesh (Zhao et al.) 2025a) and Mesh-RFT (Liu et al.) 2025) Complementary efforts have focused on areas such as acceleration (Chen et al., 2025aa Wang et al. 2025a b) and continuous level-of-detail (Zhang et al. 2025). Despite these significant advances, all existing methods in this domain are fundamentally confined to generating triangular meshes. This reveals a critical gap between the ofte--o--the-a art in generative modeling and the practical need for industry-stane standadd quadrilateral assets. Unlike the above works, QuadGPT makes the first attempt to bridge this gap, presenting scalable generative model for native quadrilateral mesh generation.
3

---

Published as a conference paper at ICLR 2026
1
Stage 1: Mesh Generation Pre-training
Stage 2: Reinforcement Poststraining
Hourglas Transformer
Tepology Compure
Reference Mesh Decoder
Auto- Regressive
r/o suoree
Mesh Decoder
Powe
V.8. Mp i Mr
Sample
Surbhcn
Policy Mesh Decoder
x2000
Jolaz
Optimbee
reject
Truncated DPO
(40non B
Imput
(a)
Rop Pin live
ly selpammarld
Figure 3: Pipeline of QuadGPT. First, an autoregressive Hourglass Transformer is pre-traein pre-trained to generate mesh sequences conditioned on an input point cloud. Subsequently, the model fine-tuned using Truncated Direct Preference Optimization (tDPO), where preference dataset is automatically constructed by comparing truncated sequences via novel topological reward.
QUADGPT
Our approach, QuadGPT, introduces the first autoregressive framework for the direct generation of
native quadrilateral and mixed-element meshes. The methodology consists of three core pillars: (1)
unified serialization scheme represent mixed-topology meshes single token sequence; (2)
a powerful autoregressive architecture for generative pre-training; and (3) tDPO stage with a novel topological reward for direct topological optimization. An overview of our pipeline is presented in Figure
3.1 UNIFIED SERIALIZATION FOR MIXED-ELEMER MESHES
We formulate mixed-element mesh generation as a sequence prediction problem, transforming a mesh M into single, linear sequence of discrete integer tokens. Our serialization scheme is de- signed to be canonical and to uniformly handle both triangular (n = 3) and quadrilateral (n = 4) faces. A mesh is represented three hierarchical levels:
M (f', P2 pN1 (S(r)), S(P?), ... S(PNr))
Face Level
Token Block Level
Tpud: ..., c),..., C2.
cz ....}
Coordinate Token Level
Triangle Block
e.g. Quad Block
where S(-) the face serialization function and Tpad special padding token.
Canonical Representation. To ensure deterministic sequence for any given geometry, we first establish canonical representation. Vertex coordinates are normalized and then quantized. To minimize precision loss, we employ a high-resolution 1024-level (10-bit) quantization, mapping each coordinate to an integer in {0, 1,.... 1023). All unique vertices are then sorted lexicographically by (z, y) coordinates, and the face set is sorted accordingly. This multi-step process guarantees a unique mapping from mesh to sequence.
[-0.95, 0.95, cube
Unified Token Block Structure. The cornerstone of our approach is unified fixed-length block representation. Each face, regardless its valence, tokenized into consistent 12-token block using the padding token Tpad (integer value 1024). A quadrilateral face is tokenized by directly flattening its tokens, followed by its 3 x 3 coordinate tokens, also forming 12-token block. This design allows the model to implicitly learn the face type from the presence padding.
= 12 vertex coordinate tokens. A triangular face is prepended with three Tpad
This unified structure offers several advantages: it creates simple, highly parallelizable tokeniza- tion process that scales effortlessly; simplifies the model architecture; and allows the Trans- former naturally differentiate face types without explicit type tokens.

---

Published as a conference paper at ICLR 2026
3.2 AUTOREGRESSIVE PRE- TRAININGG
The pre-training stage is designed to teach QuadGPT the fundamental distribution of mesh geometry and connectivity by training it to predict the next token in sequence. The model is optimized using a standard cross-entropy loss objective:
Lee = CrossEntrooy(SS: -1], -1 =)),
(2)
where S is the ground-truth token sequence and $ represents the predicted logits, Our approach incorporates powerful hierarchical architecture, shape conditioning, and a specialized training strategy tailored for structured geometric data.
Hierarchical Model Architecture. Rather than treating mesh generation as generic sequence task, we leverage the inherent hierarchical structure of mesh data. To this end, we utilize the Hourglass Transformer architecture (Hao et al., 2024 Nawrot et al., 2021), which processes the input sequence at multiple levels of abstraction. Let the input token sequence embeddings be E(O) E IR LxDo, The architecture employs series of ate a computational bottleneck. The sequence is first processed by a Transformer block and then shortened by factor of 3, and subsequently by a factor of 4:
shortening layers to cre-
( Shortenn,(TannsfooreeBBBB..
(E(")) E (L/33)xD
(3)
E (2) (horreeu(ttanffoorrrfllllccc.i
E (1/12)))
(4)
This hierarchical processing enables the model to efficiently capture high-level global context in its bottleneck layers and fine-grained local details in its outer layers before upsampling back the original sequence length for prediction.
Shape and Topological Conditioning. QuadGPT's generation is guided by primary condi- tions. First, point cloud with normals, P= (p: E R Tim1 is encoded into a global shape em- bedding Eshaee by a pre-trained Michelangelo encoder (Zhao et al. (023). To ensure this geometric context remains influential throughout the generation of long sequences, the embedding supplied to the decoder via cross-attention:
N₂ 1
(5) where H represents the decoder's hidden states. Second, enable training curriculum, introduce a learnable embedding conditioned on a quad-dominance parameter E [0, I]. This parameter explicitly controls the target ratio of face types, from purely triangular 0) to mixed quadrilateral (r = 1), providing the mechanism for our curriculum learning strategy.
H' CrossAt (H, Eshape, Eshape);
Training Strategy. Our training strategy combines truncated sequence training for efficiency with a novel curriculum for stability. To manage the long sequences of high-resolution meshes, we em- ploy truncated training, using fixed-length segments (e.g., 36,864 tokens) to enable efficient, large- batch processing. Furthermore, to ensure stable learning of complex quadrilateral topology, we in- troduce a curriculum learning strategy. We first initialize QuadGPT with weights from model pre- traindiied exclusively triangular meshes. We then progressively finetune this model, using our quad-dominance condition r to gradually anneal the training data distribution from purely triangular (r 0) to quad-dominant (r - 1). This graduated exposure allows the model to master basic geometric syntax before tackling the more complex rules of quadrilateral topology, significantly improving stability and convergence speed.
Data Strategy. The scarcity of dedicated quad-mesh datasets is addressed by a novel curation pipeline. Starting from diverse 3D sources, we apply automated triangle-to-quad conversion and multi-stage quality filtering to select 1.3 million high-quality models. This dataset is pivotal for training QuadGPT. Further details on our data curation can be found in Appendix A
3.3 TOPOLOGICAL REFNNEMENT WITH REINFORCEMENT LEARNING
While pre-training teaches syntactic validity, the cross-entropy loss is a local objective that cannot optimize for global, emergent properties like clean topology. To address this, we introduce rein- forcement learning (RL) stage using Direct Preference Optimization (DPO) (Rafailov et al. (003) to explicitly align our model with the topoloicaa structures preferred in professional 31) workflows.
5

---

Published as a conference paper at ICLR 2026
Topological Scoring Standard. Our alignment is guided by specialized scoring standard de- signed to evaluate the quality of generated mesh subsequences. The reward function quantifies topological integrity by primarily rewarding the formation of long, continuous edge loops (Lavg) and penalizing generation fractures (Ram).. These metrics are computed automatically, providing scalable signal for topological quality. The detailed formulation of these metrics is provided in Appendix B
Truncated Daad Post-Trainie Dost-Training. We finetune the pretrained QuadGPT policy by forming preference pairs using our topological rewards and optimizing a DPO-style objective on the collected pairs. Let xy denote the trainable policy being fine-tuned, Tee the frozen reference model. and B > 0 parameter controlling the deviation from Tef. The current policy нe produces candidates for input z, which are ranked by topological rewards to yield preference pairs (3w W) with
Lavg(yu) > Lave (31), Ricae (yw) Rirae (yr).
(6)
We assume pairwise preferences follow Bradley-Teny (BT) likelihood parameterized by im- plicit reward (y(y)z). KL-regularized policy optimization with reference Tret, the optimal policy satisfies
To(() x Te(()>>) cxp(ro(ul=)/33)
(8)
which implies the implicit reward is (up to an x-only constant c(2))):
ro(y)>)
B[oog To (ylar) log nee [z]] c(x).
Substituting equation 9 into the BT model cancels c(x) and yields
Polyve yn xx) of log
Ne (Morls)) log West (Vml
[o(wl West Wea (w (z)
(10)
where = -2)==
To make this process computationally tractable for long sequences, we train on random prefixes of length m (1,((,,,.. L}, where L the total length of the mesh. Let Mmom+r denote the sequence from the prefix to the truncation window, where is the window length (e.g., 36,864 tokens), then maximizing the BT likelihood [guation[O]givess the tDPO loss
(1) EE Em log (3 log
Iee (hee [x|z) Jual FoorBBr.mmine Bar.mmm+ x)) Kurl Pt,
log (M (x)
(11)
IDPO optimizes each face sequence block S(P) within the truncation window. This teaches QuadGPT to make locally optimal decisions that lead to globally superior topology. After tDPO optimization, QuadGPT presents a higher quality quad-mesh with more structured edge loops and reduced fractures.
EXPERIMENTS
4.1
EXPERIMENTAL SETTINGS
Datasets QuadGPT is pretrained on curated dataset of 1.3 million quad-dominant models, as- sembled through an extensive collection, conversion. and filtering pipeline from sources including V2 (Chang et al.] 2015), 3D-FUTURE (Fu et al., (021). Objaverse (Deitke et al. 2023b). Objaverse-XX -I. (Deitke et al. 2023a) and proprietary licensed assets. For preference alignment, we construct specialized post-training dataset built upon 500 diverse, high-quality dense meshes (including both hard-surface and organic models) generated by Hunyuan3D 2.5 (Lai et al.) 2025). After an initial quality filter via rejection sampling, generate multiple candidates from these source meshes to construct an initial set of approximately 2,000 preference pairs for fine-tuning. This core set of 500 meshes serves the foundation for DPO. To enhance the model's robustness against noisy, dense inputs, point clouds during pre-training are densely sampled at 40,960 points and augmented with random perturbations.

---

Published as a conference paper at ICLR 2026
Original Mesh
Point Cloud
MeshAny
BPT
DeepMesh
Fast
Ours
Figure 4: Qualitative Comparison against Indirect Autoregressive Pipelines. The top four show results on out-of-distribution dense meshes generated by Hunyuan3D (Lai et al. 2025). while the bottom four rows showcase performance on artist-designed meshes. Baseline methods followed by tri-to-quad conversion often produce topological artifacts and lose geometric detail. QuadGPT consistently generates meshes with superior topological coherence and fidelity across both domains.
Implementation Details We pretrain QuadGPT on a cluster of 64 NVIDIA A100 GPUs for 7 days using the AdamW optimizer (Loshchilov Hutter 2019) (3) 0.9, 82 0.95) with a learning rate of le-4 and a linear warmup schedule. The Decoder is I.IB parameter model featuring 24 Transformer layers arranged three-stage hourglass architecture. This architecture employs linear downsample layers with factors of 4 and to efficiently process long sequences. The subsequent RL fine-tuning stage performed for 4 hours on the same hardware setup, using a reduced learning rate of 1e-7. Both pre-training and fine-tuning leverage truncated sequence strategy to manage the long contexts of high-resolution meshes. During inference, the architecture supports a context window of 36,864 tokens. We generate meshes using combination of top-k and nucleus (top-p) sampling with 10 and = 0.95, along with a temperature of T = 0.5, to balance output diversity and stability. To optimize performance, inference is accelerated through custom implementation of KV caching and CUDA graphs, specifically tailored for the hourglass architecture. This achieves a generation speed of approximately 230 tokens per second single A A100 GPU.

---

Published as a conference paper at ICLR 2026
Original Meeh
QuadriFlow
Ours
Original Mesh
QuadriFlov Foow
Ours
Figure 5: Qualitative Comparison against Field-Guided Method. Field-guided methods like QuawriFlow can unstable on meshes with complex topology or sharp features.
Table I: Quantitative comparison with other baselines in Artist and Dense Meshes. Our ap- proach achieves superior performance compared to existing baselines. Quadriflow" are computed only on the subset inputs for which it successfully generated mesh; its user study score incor- porates score of 0 for all failure cases.
Data Type
Dense Meshes
Artist Meshes
Metries
CD 4
HD 4
QR
US f
CD 4
HD 4
QR
US
Quadriflow* (Huang
2018
0.045 0.099
100%
1.6
0.281
0.531
100%
0.3
MeshAnythingy2 (Chen et al. 2025c)
0.153
0.394
53%
1.4
0.096
0.251
60%
2.1
BPT (Weng et al.
2025
0.115
0.283
43%
2.7
0.051
0.125
49%
3.1
DeepMesh (Zhao et al. 2025a)
0.246 0.435
64%
3.3
0.236
0.417
66%
2.8
FastMesh (Kim et al.
2025)
0.105
0.257
3%
1.1
0.052
0.141
17%
1.9
Ours
0.057 0.147
80%
4.9
0.043
0.095
78%
4.8
Baselines. We evaluate QuadGPT against two primary categories of state-of-the-at mesh gen- eration methods. The first category comprises leading autoregressive models that generate tri- angular meshes, including MeshAnythingV2 (Chen al.) (025c), BPT (Weng et al.) (025), DeepMesh (Zhao et al.) 2025a). and FastMesh (Kim et al. 2025). Since these methods are fun- damentally designed to produce triangular outputs, we apply robust frangle-o--quddrlltteel con- version algorithm like (Muntoni & Cignoni 202T) post-processing step to facilitate a fair com- parison. The second category represents specialized quad-meshing techniques, for which include the well-established field field-guided guided method QuadriFlow (Huang et al. (018)). This selection provides a comprehensive benchmark against both the latest in generative modeling and classic, topology- focused approaches.
4.2 QUALITATIVE RESULTS
We first present a qualitative comparison of QuadGPT against existing baselines across two distinct domains: challenging, out-of-distribution dense meshes from other Al models, and in-distribution, high-quality artist-designed meshes. Visual inspection is crucial, as reveals the subtle yet critical differences in topological quality that metrics alone cannot fully capture. As illustrated in Fig- ure 4 the indirect pipeline of converting triangular meshes from autoregressive baselines often struggles. These methods frequently produce meshes with significant topological artifacts, miss- ing geometric details, or overly simplified structures that fail to capture the original shape's nu- ance. In contrast, QuadGPT consistently generates meshes that are significantly more coherent and artistically plausible, producing the clean edge flow characteristic of professional work. Our model demonstrates strong robustness on challenging Al-generated assets and achieves near-perfect topological reconstruction on artist-designed meshes. Figure 5 compares our method against the beld-guided approach of QuadriFlow. The baseline exhibits significant instability on meshes with complex topology or sharp features, often resulting in severe geometric degradation catastrophic failures. QuadGPT, in contrast, demonstrates exceptional robustness, faithfully reconstructing ge- ometry while maintaining high-quality, structured topology in all examples.
8

---

Published as a conference paper at ICLR 2026
Point Cloud
Pre-Irain
Post-train
Figure 6: Effectiveness of tDPO-Pro. Our comprehensive training strategy significantly enhances both the geometric quality and structural integrity of the generated meshemssees.
Mesh
Point Cloud
TEGPT
TTIPP((Q)
TRGGT RL TRGPT- (L((Q)
QualGPT
Figure 7: Native Generation vs. Conversion Pipeline. (Q) denotes the of a triangle-to-quad conversion step. Although TriGPT employs the same RL fine-tuning to mitigate fractures, its topo- logical quality inherently constrained by the prot-progssiing conversion, yielding significantly inferior edge flow compared to our native QuadGPT.
4.3 QUANTITATIVE RESULTS
summarrzes the quantitative comparison of QuadGPT against all baselines across both artist- designed and Al-generated dense meshes. We evaluate geometric fidelity using Chamfer Distance (CD) and Hausdorff Distance (HD), topological quality via Quad Ratio (QR), and perceptual quality through a comprehensive user study (US). The results demonstrate that QuadGPT consistently and significantly outperforms competing approaches in both quality and robustness. The user study, where experts ranked the outputs of all six methods from best (5 points) worst (0 points), reveals a decisive preference for our method. This confirms that its advantages in producing production- ready assets not only quantitatively measurable but also perceptually significant.

---

Published as a conference paper at ICLR 2026
4.4 ABLATION STUDIES
4.4.1 EFFECTIVENESS OF TDPO
We analyze the impact of our DPO components by comparing three variants: standard DPO (fine- tuning on full, low-face-count meshes), tDPO (truncated training with a basic fracture penalty), and full tDPO-Pro model (truncated training with the complete topological reward). Table shows that standard DPO fails to generalize to complex meshes. In contrast, tDPO dramatically im- proves performance, while iDPO-Pro achieves the best results across all metrics. This improvement, visualized in Figure 6 provides strong empirical support for our comprehensive, topology-awand truncated DPO framework.
4.4.2 EFFECTIVENESS OF CURRICULUM -BSEDD PRE-TRAINING
We validate our curriculum learning strategy by comparing against model trained directly on quad-dominant meshes from scratch ("From Scratch"). As shown in Table 2 the "From Scratch" model struggles to converge, result- ing in poor geometric fidelity. This difficulty arises because predicting quadrilateral face is inherently more complex than predicting tri- angle, as it is topologically equivalent pre- dicting two correlated triangles simultaneously. Initializing weights from converged seangle-generatonn model ("Finetune") yields significantly superior results. This confirms that our curriculum strategy leveraging the simpler task of trian- gle generation as warmup is essential for establishing the stable geometric foundation required to master the more challenging patterns of quadrilateral topology.
Table 2: Quantitative Evaluation of Training Strat-
egy. User study scores reflect expert rankings of the five methods, ranging from (worst) to (best).
Method
CD 4
HD +
QR 1
US
f
From Scratch
0.081
0.203
75%
0.6
Finetune
0.065
0.167
72%
1.3
DPO (DPO tDPO-Pro 0.057
0.073
0.188
74%
1.1
0.061
0.156
78%
3.3
0.147
80%
3.7
4.4.3 NATIVE GENERATION VS. CONVERSION PIPELINE
To isolate the benefits of our native approach, we introduce a strong baseline, TriGPT, which gen- erates triangles that are then converted to quads. ensure a fair comparison, TriGPT shares the identical architecture, 1.3 million mesh training dataset, and tDPO reinforcement learning strategy as QuadGPT. This setup controls for all confounding variables, testing only the efficacy of the end-to-end native pipeline versus the cemeraation-then-covvessii
paradigm.
The results Table 3 and Figure 7 our hypothesis. While the highly optimized triangle baseline (TriGPT+RL) achieves slightly bet- ter geometric scores (CD/HD), it cannot match the topological quality of our native approach. QuadGPT demonstrates substantially higher Quad Ratio (QR) and, crucially, user prefer- ence score 2.6x higher than the strongest base- line. This decisive gap in perceptual quality confirms that while post-hoc conversion struggles to create coherent global structures, our end- to-end native framework excels at learning the artist-preferred topologies, validating its superior practical utility.
Table 3: Native vs. Conversion Pipeline. QuadGPT compared against strong triangle-generation base- line (TriGPT), both with and without RL. US scores are expert rankings (0-2).
Method
CD 1
HD 4
QR
US
1
TriGPT(Q)
0.062
0.160
70%
0.2
TriGPT+RL(O)
0.051
0.138
72%
0.5
QuadGPT (Ours)
0.057
0.147
80%
1.3
5 CONCLUSION
We present QuadGPT, the first autoregressive framework that directly generates native quadrilateral and mixed-element meshes. achieves state-of-the-art results generative meshing, setting new standard for both geometric fidelity and topological quality. Our scalable, neural-first approach departs from previous triangle-base based methods and conversion pipelines, which rely on heuristic post-procsssing to approximate quad topology. By leveraging unified serialization scheme and novel topology-awan awware fine-tuning stage (tDPO). QuadGPT directly optimizes for global structure, making it well-suited for the automated creation of production-ready 3D assets.
10

---

Published as a conference paper at ICLR 2026
6 ETHICS STATEMENT
This work introduces QuadGPT, an autoregressive framework for direct generation of production- ready, quad-dominant meshes. The model was trained blend of public and professionally sourced data, subjected to rigorous quality filtering. Our primary goal is to demonstrate that a scalable, end-to-end framework effective for industrial-quality mesh generation, and we encour- age the community to explore further along this scalable paradigm. To support practical adoption, a public API and online interface will be provided. The authors declare no conflicts of interest.
REPRODUCIBILITY STATEMENT
This paper presents scalable autoregressive framework for native quad-mesh generation. Although full model release not feasible at this stage, the method is comprehensively detailed in terms of data representation, network architecture, and training protocols facilitate replication and ex- tension. We emphasize that high-quality data curation, including rigorous collection, processing, and filtering of open-source data, essential for achieving comparable performance. To support validation and downstream use, we will provide public API and Code.
ACKNOWLEDGMENTS
This research was supported by fundings from the Hong Kong RGC General Research Fund (152228/23E, 162161/24E, 162116/25E, 162180/25E), National Natural Science Foundation of China (NSFC) Key Program (No.62532005), Collaborative Research Fund (No. C1042-23GF, No. 5097-25G), NSFC/RGC Collaborative Research Scheme (Grant No. 62461160332 & CRS_HKUST600/24), Research Impact Fund (No. R5011-23F), Areas of Excellence Scheme (A0E/E-601/22-R), and the InnoHK (HKGAI).
REFERENCES
Blender Foundation. Blender. URLptttpp htt.:/tttss/www..bbnddee.
D. Bommes, M. Campen, H.-C. Ebke, P. Alliez, and L. Kobbelt. Integer- -riger--riddd maps for reliable quad meshing, ACM Transactions on Graphics (TOG), 32(4):98, 2013.
David Bommes, Hcnrik Zimmer, and Leif Kobbelt. Mixed-integer quadrangulation. ACM Transac- fions Graphics, 28(3):1-10, 2009.
Marcel Campen and Leif Kobbelt. Dual strip weaving: Interactive design of quad layouts using 2014.
clastica strips. ACM Transactions on Graphics,
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information- 3d model repository. arXiv preprint ar2iv:151..03012, 2015.
Dian Chen, Yansong Qu, Xinyang Li, Ming Li, and Shengchuan Zhang. Xspecmesh: Quality- preserving auto-regressive mesh generation acceleration via multi-head speculative decoding. arXiv preprint arXiv:2507.23777 2025a.
Sijin Chen, Xin Chen, Anqi Pang, Xianfang Zeng, Wei Cheng, Yijun Fu, Fukun Yin, Billzb Wang, Jingyi Yu, Gang Yu, et al. Meshxl: Neural coordinate field for generative 3d foundation models. Advances in Neural Information Processing Systems, 37:9714) 1111111166666 2024.
Yiwen Chen, Tong He, Di Huang, Weicai Ye, Sijin Chen, Jiaxiang Tang, Zhongang Cai, Lei Yang, Gang Yu, Guosheng Lin, and Chi Zhang. Meshanything: Artist-created mesh generation with autoregressive transformers. In The Thirteenth International Conference on Learning Represen- tations, 2025b. UtLhtttss: http:tttt///eeereiiiii http/ppereeeeeeeee net. forum? d=KGZAs 8VcMM
Yiwen Chen, Yikai Wang, Yihao Luo, Zhengyi Wang, Zilong Chen, Jun Zhu, Chi Zhang, and Gu- osheng Lin. Meshanything v2: Artist-created mesh generation with adjacent mesh tokenization. IEEE International Conference Computer Vision, 2025c.
11

---

Published as a conference paper at ICLR 2026
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objaverse-xl: xl: A universe of 10m+ 3d objects. Advances in Neural Information Processing Systems, 36:35799-358135 2023,
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. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 13142-13153, 2023b.
O. Diamanti, A. Vaxman, D. Panozzo, and O. Corkine-Horuung Integrable polyvector fields, ACM Transactions on Graphics (TOG), 34(4):38, 2015.
Alexander Dielen, Isaak Lim, Max Lyon, and Leif Kobbelt. Learning direction fields for quad mesh generation. In Computer Graphics Forum, volume 40, pp. 181-191. Wiley Online Library, 2021.
Qiujie Dong, Jiepeng Wang, Rui Xu, Cheng Lin, Yuan Liu, Shiqing Xin, Zichun Zhong, Xin Li, Changhe Tu, Taku Komura, et al. Crossgen: Learning and generating cross fields for quad mesh- ing. arXiv preprint arXiv:2506.070000
2025a.
Qiujie Dong, Huibiao Wen, Rui Xu, Shuangmin Chen, Jiaran Zhou, Shiqing Xin, Changhe Tu, Taku Komura, and Wenping Wang. Neurcross: A neural approach to computing cross fields for quad mesh generation. ACM Trans. Graph., 44(4), 2025b.
Hans-Christian Ebke, Patrick Schmidt, Marcel Campen, and Leif Kobbelt. Interactively controlled quad remeshing of high resolution 3d models. ACM Transactions on Graphics, 35(6):218:1- 218: 13, November 2016.
Exoside. Exoside quad remesher: Automatic quad remeshing plugin, 2019. URL https://
exoside com/
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, 129:
3313-3337, 2021.
Zekun Hao, David Romero, Tsung- Yi Lin, and Ming- Yu Liu. Meshtron: High-fidelity. artist- 3d mesh generation at scale. arXiv preprint arXiv:2412.09544, 2024.
Xianglong He, Zi-Xin Zou, Chia-Hao Chen, Yuan-Chen Guo, Ding Liang, Chun Yuan, Wanli Ouyang, Yan-Pei Cao, and Yangguang Li. Sparseflex: High-resolution and arbitrary-topology 3d shape modeling, arXiv preprint arX1v:2503.21732, 2025.
Jingwei Huang, Yichao Zhou, Matthias Niessner, et al. Quadriflow: A scalable and robust method for quadrangulation. Computer Graphics Forum, 2018. ISSN 1467-8659.
Wenzel Jakob, Marco Tarini, Daniele Panozzo, and Olga Sorkine- Instant field-aligned meshes. ACM Transactions on Graphics, 34(6):189, 2015.
Tengfei Jiang, Xianzhong Fang, Jin Huang, al. Frame field generation through metric customiza- tion. ACM Transactions on Graphics, 34(4):1-11, 2015.
Jeonghwan Kim, Yushi Lan, Armando Fortes, Yongwei Chen, and Xingang Pan. Fastmesh: Efficient artistic mesh generation via component decoupling. arXiv preprint 2xX::25008.1188 2025.
Zeqiang Lai, Yunfei Zhao, Haolin Liu, Zibo Zhao, Qingxiang Lin, Huiwen Shi, Xianghui Yang, Mingxin Yang, Shuhui Yang, Yifei Feng, et al. Hunyuan3d 2.5: Towards high-fidelity 3d assets generation with ultimate details. arXiv preprint arXiv:2506. 16504, 2025.
Biwen Lei, Yang Li, Xinhai Liu, Shuhui Yang, Lixin Xu, and Chunchao Guo. Hunyuan3d studio: End-to-end ai pipeline for game-ready 3d asset generation, 2025.
Weiyu Li, Jiarui Liu, Rui Chen, Yixun Liang, Xuelin Chen, Ping Tan, and Xiaoxiao Long. Crafts- man:
mesh generation with 3d native generation and interactive geometry refiner. arXiv preprint 400 149.99999 2024.
12

---

Published as a conference paper at ICLR 2026
Yang Li, Victor Cheung, Xinhai Liu, Yuguang Chen, Zhongjin Luo, Biwen Lei, Haohan Weng, Zibo Zhao, Jingwei Huang, Zhuo Chen, et al. regresegre surface cutting. arXiv preprint 200.22566.10177, 2025a.
Yangguang Li, Zi-Xin Zou, Zexiang Liu, Dehu Wang, Yuan Liang. Zhipeng Yu, Xingchao Liu, Yuan-Chen Guo, Ding Liang, Wanli Ouyang, et al. Triposg: High-fidelity 3d shape synthesis using large-scale rectified flow models. arXiv preprint wrXiv:2502.06608, 2025b.
Zezeng Li, Zhihui Qi, Weimin Wang, Ziliang Wang, Junyi Duan, and Na Lei. Point2quad Gen- erating quad meshes from point clouds via face prediction. IEEE Transactions on Circuits and Systems for Video Technology. 2025c.
Zhihao Li, Yufei Wang, Heliang Zheng, Yihao Luo, and Bihan Wen, Spare3d: Sparse representa- tion and construction for high-resolution 3d shapes modeling, arXiv preprint arXiv:2505. 14521, 2025d.
Haotong Lin, Sili Chen, Junhao Liew, Donny Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang. Depth anything 3: Recovering the visual space from any views. arXiv preprint arXiv:2511.10647, 2025.
Stefan Lionar, Jiabin Liang, and Gim Hee Lee. Treemeshgpt: Artistic mesh generation with au- toregressive tree sequencing. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 26608-26617, 2025.
Jian Liu, Jing Xu, Song Guo, Jing Li, Jingfeng Guo, Jiaao Yu, Haohan Weng, Biwen Lei, Xianghui Yang. Zhuo Chen, et al. Mesh-rft: Enhancing mesh generation via fine-grained reinforcement fine-tuning, arXiv preprint arXiv:2505.1677 2025.
William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. Seminal graphics: pioneering efforts that shaped the field, pp. 347-353. 1998.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. International Confer- ence on Learning Representations, 2019. URL
http:/ewwiewweeeeeeeeee net formm/ ?id=
BkG6RiCqY7
Alessandro Muntoni and Paolo Cignoni. PyMeshLab, January 2021.
Piotr Nawrot, Szymon Tworkowski, Michat Tyrolski, Lukasz Kaiser, Yuhuai Wu, Christian Szegedy, and Henryk Michalewski. Hierarchical transformers are more efficient language models. arXiv preprint arXiv:2110.13711, 2021.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly reward model. Advances in Neural Information Processing Systems, 36:53728-53741, 2023.
Tianchang Shen, Jun Gao, Kangxue Yin, Ming- Yu Liu, and Sanja Fidler. Deep marching tetrahedra: hybrid representation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems, 34:6087-6101, 2021.
Tianchang Shen, Jacob Munkberg, Jon Hasselgren, Kangxue Yin, Zian Wang, Wenzheng Chen, Zan Gojeic, Sanja Fidler, Nicholas Sharp, and Jun Gao. Flexible isosurface extraction for gradient- based mesh optimization. ACM Trans. Graph., 42(4):37-1, 2023.
Yawar Siddiqui, Antonio Alliegro, Alexey Artemov, Tatiana Tommasi, Daniele Sirigatti, Vladislav Rosov, Angela Dai, and Matthias Nießner. Meshgpt: Generating triangle meshes with decoder- only transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 19615-19625, 2024.
Gaochao Song, Zibo Zhao, Haohan Weng, Jingbo Zeng, Rongfei Jia, and Shenghua Gao. Mesh silksong: Auto-regressive mesh generation weaving silk. arXiv preprint arXiv:2507.02477, 2025.
Stefan Stojanov, Anh Thai, and James M. Rehg. Using shape to categorize: Low-shot learning with explicit shape bias. In CVPR, pp. 1798- 1808, June 2021.
13

---

Published as a conference paper at ICLR 2026
Jiaxiang Tang, Zhaoshuo Li, Zekun Hao, Xian Liu, Gang Zeng, Ming- Yu Liu, and Qinsheng Zhang. Edgerunner: Auto-regressive auto-encoder for artistic mesh generation. In The Thirteenth In- ternational Conference Learning Representations, 2025. http::tt////// http:::///peeeevieer net/ /ert/form?? i cta
Marco Tarini, Nico Pietroni, Paolo Cignoni, Daniele Panozzo, and Enrico Puppo. Practical quad mesh implification. Computer Graphics Forum (Special Issue of Eurographics 2010 Conference), 29(2), 200.
Tripo Al Team. Tripo ai: Intelligent remesher, 2025. URL workspace/ remesh
Hanxiao Wang, Biao Zhang, Weize Quan, Dong-Ming Yan, and Peter Wonka. iflame: Interleaving full and linear attention for efficient mesh generation. arXiv preprint
2025a.
Yuxuan Wang. Xuanyu Yi, Haohan Weng. Qingshan Xu, Xiaokang Wei, Xianghui Yang. Chunchao Guo, Long Chen, and Hanwang Zhang. Nautilus: Locality-aware autoencoder for scalable mesh generation. IEEE International Conference on Computer Vision, 2025b.
Haohan Weng, Zibo Zhao, Biwen Lei, Xianghui Yang, Jian Liu, Zeqiang Lai, Zhuo Chen, Yuhong Liu, Jie Jiang, Chunchao Guo, al. Scaling mesh generation via compressive tokenization. In Proceedings of the Computer Vision and Putlern Recognition Conference, pp. 11093-11103, 2025.
Shuang Wu, Youtian Lin, Feihu Zhang, Yifei Zeng, Jingxi Xu, Philip Tomr Xun Cao, and Yao Yao. Direct3d: Scalable image-to-3d generation via 3d latent diffusion transformer. arXiv preprint arXiv:2405. 14832, 2024.
Shuang Wu, Youtian Lin, Feihu Zhang, Yifei Zeng, Yikang Yang, Yajie Bao, Jiachen Qian, Siyu Zhu, Xun Cao, Philip Torr, et al. Direct3d-s2: Gigascale 3d generation made easy with spatial sparse attention. arXiv preprint arXiv:2505. 17412, 2025.
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen. Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. arXiv preprint arXiv:2412.00500 2024.
Xinhao Yan, Jiachen Xu, Yang Li, Changfeng Ma, Yunhan Yang, Chunshi Wang. Zibo Zhao, Ze- qiang Lai, Yunfei Zhao, Zhuo Chen, et al. X-part: high fidelity and structure coherent shape decomposition. arXiv preprint arXiv:2509.0mm 08643, 2025.
Chongjie Ye, Yushuang Wu, Ziteng Lu, Jiahao Chang, Xiaoyang Guo, Jiaqing Zhou, Hao Zhao, and Xiaoguang Han. Hißdgen: High-fidelity 3d geometry generation from images via normal bridging. arXiv preprint arXiv:2503. 222366, 2025.
Biao Zhang, Jiapeng Tang, Matthias Niessner, and Peter Wonka. 3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models. ACM Transactions On Graphics (TOG), 42(4) 2023.
Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions on Graphics (TOG), 43(4):1-20, 2024.
Xiang Zhang, Yawar Siddiqui, Armen Avetisyan, Chris Xie, Jakob Engel, and Henry Howard- Jenkins. Vertexregen: Mesh generation with continuous level of detail. IEEE International Conference on Computer Vision, 2025.
Ruowen Zhao, Junliang Ye, Zhengyi Wang, Guangce Liu, Yiwen Chen, Yikai Wang, and Jun Zhu. Deepmesh: Auto-regressive artist-mesh creation with reinforcement learning. IEEE International Conference on Computer Vision, 2025a.
Zibo Zhao, Wen Liu, Xin Chen, Xianfang Zeng, Rui Wang. Pei Cheng, Bin Fu, Tao Chen, Gang Yu, and Shenghua Gao. Michelangelo: Conditional 3d shape generation based shape-image-text aligned latent representation. Advances in neural information processing systems, 36:73969- 73982, 2023.
14

---

Published as a conference paper at ICLR 2026
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:2501.1202,, 2025b.
15

---

Published as a conference paper at ICLR 2026
APPENDIX
A DATASET CURATION PIPELINE
The foundation of QuadGPT a large-scale, high-quality dataset. This section details our three- stage pipeline for its construction: data sourcing and augmentation, multi-stage quality filtering, and final compilation.
A.1 DATA SOURCING AND AUGMENTATION
The foundation of QuadGPT is a large-scale, high-quality dataset. This section details our three- stage pipeline for its construction: data sourcing and augmentation, multi-stage quality filtering, and final compilation. We aggregate 3D models from diverse sources, including Objaverse-XI. DDettke et al. (2023a) and professional modeling repositories. To substantially augment our training data with quad-dominant examples, we developed Triangle-o-Qquudriltteel Quadrilateral Conversion Operator. Given triangle mesh My, the goal is to find an optimal set of internal edges Bint C Mr to dissolve. We formulate this as an Integer Linear Programming (ILP) problem. For each edge Ema define a binary decision variable Te E {0,1) 1}. The optimization objective is:
maximize
Σ
De
(12)
oE Fax
subject to the constraint that for any triangle EMrs
Σ
Ze 51
(13)
eEodges(t)
The weight we is quality score that favors dissolving edges that form well-conditioned quadrilat- erals. Constraint [3 ensures each triangle participates in most one merge operation, preserving manifold integrity. Following this conversion, we perform crucial geometric validation step: any newly formed quadrilateral exhibiting maximum interior angle greater than 150 degrees deemed geometrically unstable and is split back into its two original constituent triangles. This ensures our automated pipeline produces only high-quality, well-shaped quadrilaterals.
A.2 MUL.II-TAAG QUALITY FILTERING
The initial dataset, while extensive, contains numerous low-quality models. We employ a rigorous, two-stage filtering pipeline ensure data fidelity. First, we apply suite of rule-hased operators to discard models with specific flaws, including high-aspect-ratio faces and patterns characteristic of poor automated decimation. critical component our Fractured Geometry Detector (Algo- rithm []. which identifies open seams by performing a "test weld" and checking for a significant reduction in edge count without loss faces. Second, to filter for aesthetic quality, we trained vision- based quality assessment model on a manually annotated corpus of 100,000 models, allow- ing us to automate the removal of assets with poor, albeit technically valid, edge flow.
A.3 FINAL DATASET COMPILATION
Following this pipeline, we selected models with face counts between 500 and 20,000 to form our final training dataset, comprising 1.3 million high-quality, production-ready 3D models.
16

---

end end end Published as a conference paper at ICLR 2026
Algorithm 1: Fractured Geometry Detection
Input MMMh Object M
Output: Boolean 'has fracture'
M" P)) / Remove dupl cates and loose geometry
Components
for component in Components do
caum_vertices(cee (ertpcesscompoeent < Traumii then
continue;
/* Ignore small fragments
end if not IsManijoldcoomponenti then
Exctore: Fbcfore f hotpl//cccnueaeoouuuooooooooooo
component' + Merge Vertices (cmmpeeeeoeeeemo
(component, threshold Tweld);
Eatier, Fateer t
if Enefure Eater > Tedge detu deliu and Fbefore Fater then
return true;
/* Fracture detected
return false;
Algorithm 2: Fracture Count Calculation
Input Partial Mesh M& FR)
Output: Fracture Count Cirae
/* Define generation frontier from the last generated face. */ flast last face the sequence of FR: if fas is null then return 0:
end
Ufrontier +- mil (v.y);
Count boundary faces at or below the frontier.
Fanandary t- set of faces in F& on the mesh boundary;
Ctea + 0:
for face in Fboundary do
Au E f.u.y S Mrooiier then
Ctrac &- Citac
end
end
return Cfrac
B TOPOLOGICAL QUALITY METRICS FOR TRUNCATED SEQUENCES
This section provides a formal definition of the metrics used to evaluate the topological quality of partially generated quadrilateral meshes. These metrics are specifically designed to operate on truncated sequences, forming the basis of the reward signal for our Direct Preference Optimization (DPO) fine-tuning stage.
B. FRACTURE DETECTION
A primary failure mode in autoregressive generation is fractures. Since our serialization is canoni- cally ordered bottom-to-top, we detect fractures by identifying boundary faces at the current gener- ation frontier. Algorithm formalizes this by defining the frontier based the lowest Y-coordinate of the last generated face. A non-zero count indicates failure to generate an adjacent face in the expected upward direction, signaling topological break.
17

---

Published as a conference paper at ICLR 2026
B.2 QUANTIFYING EDGE FLOW: QUAD RINGS AND LINES
The hallmark of bigh-quality quad topology is structured edge flow, which we quantify by identi- fying two key structures: Quad Rings (closed loops of faces) and Quad Lines (open strips), as illustrated in Figure 8 forrmaliiis an edge-based traversal that "walks" across adjacent quadrilateral faces. If path terminates, it is classified edge, it forms Quad Ring. From the discovered sets of rings (R) and lines (I), we compute the final reward components: the ratio of faces participating in closed rings and the average length of the open lines, both of which are maximized during DPO.
Quad Line; if it returns its starting
Algorithm 3: Quad Ring and Line Discovery Input Partial Mesh Ma
Output: Set of Quad Rings IR (face lists), Set of Quad Lines L, (face lists)
- 0,
Pprocessedd f- 0;
for edge Caar in Mg.edges do ssart & Eprocssed then
path-faces +- 0; path_edges 4- 0;
current.edge + Cstant:
while current current_edge + null and perrent_edge & patheedges do
add current edge to path edges;
adj_qu adj_quad= quadrilateral face adjacent current_edge;
if -qdj-quad exists then
add adj-quad to faces;
current edge < edge opposite to current edge in adj quad; end else
currenteedge < null; end end Eprocessed awwpesssoaad(ppthh.deees
Classify the discovered path based on closure. if current-ede .dge is null or current-edge Cnen then add path_face to L;
end
else
add path fache to R;
end
end end
return R, L
C MORE RESULTS
To further demonstrate the superiority of our direct generation approach, we provide extensive quali- tative comparisons in Figure Figure 10 These results highlight the robustness and high fidelity of QuadGPT across a wide variety complex geometries. Additionally, to illustrate the structural integrity of our generated models, present multi-view renderings of several samples in Figure II] Finally, we compare our method with closed-source, commercial quad mesh generation techniques, including Tripo Team 2025 and Quad Remesher Exoside (2019) in Figure T2
D ADDITIONAL EXPERIMENTAL ANALYSIS
In this section, we provide further analysis complement the experiments in the main paper.
18

---

Published as a conference paper at ICLR 2026
Figure 8: An illustration of Quad Ring which is closed loop of quadrilateral faces. Our tDPO reward is designed to encourage the formation of such desirable, structured patterns.
D. IMPACT OF CURRICULUM LEARNING AND TOPOLOGY-AWARE REWARDS
Figure 13 a) compares the next token prediction loss curves between from-scratch quadrilateral mesh training and fine-tuning from pre-trained triangle mesh weights. Fine-tuning achieves faster convergence and lower loss, confirming that inheriting triangle mesh knowledge provides better ini- tialization for quadrilateral generation. Figure 13 presents the reward curves during DPO training, where tDPO-Pro approach with truncated edge loop optimization consistently achieves the high- est reward, validating the effectiveness our complete topological reward design. The progressive improvement from DPO to iDPO and finally tDPO-Pro demonstrates the eumulative benefits of our methodological contributions.
D.2 THE INHERENT LIMITATIONS OF TRIANGLE-TO-QUAN CONVERSION
To illustrate the limitations any post-hoe conversion pipeline, we conduct a best-case scenario experiment. We begin with high-quality artist-created quad mesh and triangulate it. This process represents an irreversible information loss: the artist's original topological intent becomes ambigu- ous, creating complex combinatorial problem that heuristic algorithms struggle solve.
As shown in Figure 14, we compare our own ILP-based operator against the default methods in MeshLab Tarini al. (200) and Blender Blender Foundation While our optimized operator performs best, none of the methods can perfectly recover the original edge flow, leaving behind topological artifacts.
19

---

Published as a conference paper at ICLR 2026
Original Mesh
Point Cloud
MeshAny
BPT
DeepMeesh
FastMesh
Ours
Figure 9: Extended Qualitative Comparison against triangle generation.
This challenge is significantly amplified when the input an Al-generated triangle mesh, which often has an edge flow unsuitable for conversion, This compounding effect of a suboptimal input and an imperfect algorithm reinforces the need for native generation approach.
in our TriGPT experiments (Figure 77).
Furthermore, algorithms from adjacent fields like Blossom Quad (?), designed for 2D finite element analysis, too restrictive for complex 3D surfaces. Its perfect-matching requirement caused it to fail on all but our simplest test cases, underscoring the need for a domain-specific, learning based solution like QuadGPT.
20

---

Published as a conference paper at ICLR 2026
Original Mesh
QuadriFlow
Ours
Original Mesh
QuadriFlow
Ours
Figure 10: A qualitative comparison against the field- geld-guided method QuadriFlow. This example highlights the superior robustness and topological quality of our direct generative approach.
D.3 ANALYSIS OF COMPRESSED vs. DIRECT COORDINATE REPRESENTATION
critical design choice in autoregressive mesh generation is the tokenization strategy. Methods like DeepMesh (Zhao et al., 2025a) and BPT (Weng et al. (202)) employ compressed representations. Our experiments with these methods on triangle meshes confirmed a similar finding: compressed tokenizers lead to faster initial convergence due to shorter sequence lengths. However, we argue that faster convergence does not necessarily lead to better final performance. As shown in Table our ex- tensive experiments at the I I B--aaameterr scale show that a simple, direct coordinate representation ultimately achieves superior results in both geometric fidelity and topological quality, finding strongly corroborated by user study. We hypothesize that overly complex, "over-designed" tok- enizers can introduce an unintended inductive bias, constraining the model's expressive capacity and limiting its performance ceiling, especially when trained on large, diverse datasets. This observation aligns with broader trend in deep learning, demonstrated by models like DepthAnything V3 (Lin) al. 2025), where simple, highly scalable architectures often outperform more complex designs when sufficient data and compute are available. For QuadGPT, we therefore prioritized the higher performance ceiling of the direct representation, confident that techniques like truncated training effectively manage the longer sequence lengths.
D.4 ABLATION ON TRAINING DATA
Table Quantitative Comparison: Tokenization on Triangle Meshes. Direct coordinate representa- tion (TriGPT) outperforms a compressed BPT-style tok- enizer (TriGPT+BPT), User Study scores reflect expert rankings from 0 (worst) 1 (best). (Q) denotes outputs were converted to quads for evaluation.
To disentangle the impact of our methodology from curated dataset, we conduct data ab- lation study. We train a variant, QuadGPT-OS, using only the publicly available portions of our dataset (e.g.. ShapeNet Chang et al. (2015), (2023a)), subjected to the same rigorous filtering pipeline described in Section A This experiment serves to an- swer critical question: can robust, scalable methodology achieve state-of-the-art results even when limited to open-source data?
Objaverse-XX XL Deitke
Method
CD 1
QR 1
US
TRGPT-BPT (Compressed)
0.078
0.198
68%
0.3
TriGPT (Direct Coord.)
0.062
0.160
70%
0.7
The results, shown in Figure 15 are twofold and unequivocal. First, QuadGPT-OS still signif- icantly outperforms all prior autoregressive baselines. This confirms that our core contribu- tions- the scalable architecture, curriculum learning strategy, and tDPO refinement-are the pri- mary drivers of our model's high performance. It proves that our method is not merely product of better data, but fundamentally more solid approach to the problem.
Second, our full model, trained on the complete 1.3 million mesh dataset, demonstrates a further substantial improvement in quality over QuadGPT-OS. This highlights key insight: while perior methodology can establish new state of the art on public data, the full potential of truly scalable approach is unlocked through careful, large-scale data curation. Our work demonstrates the
21

---

Published as a conference paper at ICLR 2026
Image
Dense Mesh
Point Cloud
Degree 0
Degree 45
Degree 90
Degree 145
Degree 180
Figure 11: Multi- View Renderings of Generated Meshes.
synergistic impact of advancing both the model and the data to push the frontier of production- 3D generation.
E DOWNSTREAM APPLICATIONS
This section demonstrates the advantages of high-quality topology in downstream applications, such UV mapping and animation. Thanks superior quad mesh structure, the models facilitate
22

---

Published as a conference paper at ICLR 2026
Image
Original Mesh
Tripo
Quad Remesher
Ours
Figure 12: Comparison with closed-source commercial quad mesh generation methods. Our approach preserves more geometric details, especially edge loops, which are crucial for UV seg- mentation and animation. Compared to Blender's remeshing plugin, method produces compact faces.
Pretrain Loss Compartson
Positive Reward Comparison
Fr2m Scracc
g
12.0
ning Stops
Taining Stops
Figure 13: Training Dynamics Analysis. (a) Next token prediction loss comparison between from- scratch quadrilateral mesh training and fine-tuning from pre-trained triangle mesh weights. Fine- tuning demonstrates faster convergence and lower loss. (b) DPO reward curves for different vari- ants, showing that our tDPO-Pro approach achieves the highest reward with truncated edge loop optimization.
high-quality UV unwrapping Li et al. (2025a). Furthermore, the abundance of edge loops enables more natural mesh deformation.
F LIMITATION AND FUTURE WORK
Despite the promising results of QuadGPT, several limitations present important avenues for future research.
Domain Gap and Part-based Generation. Our framework operates within the prevailing two- stage paradigm, where topology generation follows geometry generation. This creates critical domain gap: the model is trained point clouds from clean, craist- assets but is often de- ployed on noisy point clouds derived from Al-generated implicit fields. This mismatch can lead to fractures and other failures complex, out-of-distribution shapes, shown in Figure 17 A
23

---

Published as a conference paper at ICLR 2026
Original Quad
Quad-to-Ii Trrrr
Tri2Quad(M)
Tri2Quad(B)
Tri2Quad (Or))
Figure 14: The Irreversibility of Triangulation and Limitations of Conversion Algorithms. (a) high-quality artist-created quad mesh with clean edge flow. (b) The same mesh after triangu- lation the ground truth topological information is now ambiguous. (c-e) The results of applying different tri-to-quad conversion algorithms (b). While our ILPed operator (e) produces bet- ter result than the default methods in MeshLab (c) and Blender (d). none can perfectly recover the original topology, resulting in broken edge loops and artifacts. This demonstrates the fundamental limitations of post-hoc conversion.
promising direction is to develop an end-to-end model that co-generates geometry and topology, bypassing the intermediate dense mesh entirely. Furthermore, since professional assets are inher- ently part-based, integrating part-aware generation, potentially building on recent advances in part generation (Yan et al. 2025), could significantly improve robustness and fidelity.
24

---

Published as a conference paper at ICLR 2026
Original Mesh
Point Cloud
MeshAny
BPT
DeepMesh
FastMesh
QuadGPT-OS
QuadGIT
Figure 15: Qualitative results of our data ablation study. "QuadGPT-OS" is trained exclusively on filtered public datasets, while "QuadGPT" uses our complete curated dataset. "QuadGPT-OS" already demonstrates a significant improvement in topological coherence and geometric detail over prior baselines. The full model shows further refinement, particularly in achieving cleaner, more professional edge flow, validating the scalability of our approach with high-quality data.
Lack of Controllable Polygon Number. QuadGPT currently cannot explicitly control the final polygon count; attempts add simple number condition were ineffective. This limits its utility production, where generating assets with specific polygon budgets for LODs is essential. Future work must develop robust control mechanism.
Advancing the Reinforcement Learning Framework. Our current implementation uses off- policy DPO for its simplicity and stability. However, our programmatic reward, while effective at enforcing structural rules like edge loops, cannot capture the full nuance of artistic preference. Future work could explore two key directions. First, developing reward model trained on human preference data from professional artists could provide richer, more aligned optimization signal. Second, investigating more advanced, potentially online RL algorithms could further elevate the quality and complexity of the generated topology.
G LLM USAGE STATEMENT
The authors used Gemini 2.5 Pro exclusively for grammar checking and language polishing of the manuscript text. All technical content, experimental design, data analysis, and scientific conclusions the original work of the authors. The LLM was not involved in generating scientific ideas, conducting experiments, or interpreting results.
25

---

Published as a conference paper at ICLR 2026
Quad Mush
UV Mapping
Quad Mesh
Animation
(a)
(b)
UV mapping and animation deformation comparison. (a) UV unwrapping result a quad mesh generated by our method. (b) Animation deformation comparison: the top row shows results mesh from Tripo, while the bottom row presents ours. Our method captures geometric details more effectively and produces more edge loops, leading to noticeably more realistic defor- mation.
Figure 16:
Figure 17: Failure Cases and Limitations, Our model can sometimes struggle to generalize to out- of-distribution point clouds sampled from Al-generated assets, particularly for architectural models with sharp features, which can result in fractures or incomplete geometry.
26


Reviews:
[{"review_id": "w9Icudi0Iw", "reviewer": "Reviewer_87rP", "summary": "This paper presents an auto-regressive mesh generation model that can produce both triangular meshes and quadrilateral meshes. The model is trained with a multi-stage training strategy: 1) pre-training on triangular meshes only, 2) fine-tuning on a mixture of triangular and quadrilateral meshes, 3) reinforcement learning post-training to enhance the topology of the generated meshes.", "questions": "1. As one of the advantages of generating quadrilateral meshes is to save tokens (representing two triangles ~2 x 9 coords with one quadrilatero ~12 coords) It is also interesting to study how much the current model can represent complex geometries with less tokens compared to previous methods.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. This paper represents the first work on quadrilateral mesh generation. It proposes a hybrid representation for triangles and quadrilaterals, enabling the generation of artist-like 3D meshes input point clouds.\n\n2. The paper presents a multi-stage end-to-end training framework, which incorporates both traditional next-token-prediction and sequence-level reinforcement learning supervision. The generation capability is improved in a targeted manner.\n\n3. As observed in the metrics and qualitative study, the model achieves superior results. The paper also provides many potential applications of the generated meshes, highlighting its practical usefulness.", "weaknesses": "1. As the model supports both triangular and quadrilateral mesh generation, it would be better if treated triangular mesh generation as an evaluation task. It is interesting to study: \n    - whether training on the mixed representation helps the generation of triangular meshes, \n    - whether the proposed reinforcement learning also improves the generation of triangular meshes.\n\n    It is also a natural request since previous works mainly focus on generating triangular meshes.\n\n2. How well model follows the `quad-dominance parameter`? Now that we have mixed representations, *i.e.* triangular and mixed triangular and quadrilateral representations. Is the `quad-dominance parameter` sufficient for conditioning the model to generate the desired mesh representation? It would be better to show different generation results (triangular and mixed representations) for the same geometry.", "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": "ZtOtzPpcft", "reviewer": "Reviewer_cNgg", "summary": "1 The paper extends prior triangle-based mesh generation frameworks to quadrilateral mesh generation by introducing an additional vertex in the sequence representation.\n2 The paper propose a topology-aware reinforcement learning fine-tuning method (tDPO) to enhance QuadMesh quality", "questions": "I could like the author to report NC and |NC| similar to other papers.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Indeed, this paper extends existing triangle-based mesh generation methods to quadrilateral mesh generation.\n\n2. The paper introduces a reinforcement learning strategy with one reward function that encourages long continuous edges and one penalty function that discourages fractures, aiming to improve mesh quality.", "weaknesses": "Although this paper is the first to extend autoregressive mesh generation to quadrilateral meshes, the extension—essentially adding one additional vertex—feels rather trivial and not strongly innovative.\n\nThe training process is also computationally expensive, requiring 64 A100 GPUs for 7 days, which makes it difficult for other researchers to reproduce the results. If the authors do not plan to release the code and weights, the paper’s academic contribution will be quite limited. Furthermore, the model is trained on proprietary licensed assets, which further reduces reproducibility and makes independent verification challenging.\n\nOverall, this paper follows a typical data-driven approach—collect/label/clean a large dataset and train a large model. The methodology itself is not particularly innovative or inspiring, and it is unclear how future researchers could benefit from it.", "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": "2s0WtO1Mhj", "reviewer": "Reviewer_SA17", "summary": "This work introduces QuadGPT, an autoregressive framework for direct generation of quadrilateral meshes. The input condition is a point cloud with normals. The authors propose a unified representation through padding, supporting mixed-element topologies (triangles + quads). The model is built upon an hourglass architecture. The pretraining loss is a standard cross-entropy loss. The model is further finetuned by a reinforcement learning approach with truncated direct preference optimization, rewarding coherent edge loops. Experiments show that QuadGPT generates higher quality quad meshes when compared to prior methods, both quantitatively and qualitatively.", "questions": "- As mentioned in the weaknesses, the comparisons to triangle mesh generation baselines need to be strengthened. For example, in the training strategy, the authors already pretrained a model exclusively on triangle meshes (L247-248). Combining this model with triangle-to-quadrilateral conversion could be a strong baseline, reducing the difference of training data and model capacity used in other triangle mesh generation baselines.\n- The authors introduced a learnable embedding for a quad-dominance parameter to control the target ratio of face types. However, L990 seems to indicate that this is not effective in practice, and there is no corresponding quantitative analysis.\n- In L111, the authors claim that QuadGPT bridges the gap between text/image inputs and production-ready 3D artist meshes. However, the majority of the results in the main text are generated from point clouds.\n- There is no promise of code and data release. Though the authors mention that a public API will be provided, access to the full model weights and training data is important for reproducibility and follow-up research.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "- This work introduces an end-to-end learning-based framework for direct quad mesh generation from point clouds. This is challenging as meshes have complex structures with significantly large numbers of face and vertex elements, and forming coherent edge loops as in professional-crafted quad meshes is difficult.\n- To promote clean topologies in the generated quad meshes, the authors introduce a reinforcement learning stage, optimizing a direct preference optimization objective rewarding long, coherent edge loops. To handle long sequences, the authors use a truncated, local window-based approach.", "weaknesses": "- The novelty of the proposed method is somewhat limited: the straightforward padding in the sequence representation to support triangles + quads, the hierarchical hourglass architecture from MeshTron [Hao et al. 2024], the direct preference optimization already proposed for mesh generation in DeepMesh [Zhao et al. 2025]. Overall, the proposed method seems to be a simple extension of those existing works to quad mesh generation.\n- The experimental evaluation is less comprehensive. Comparisons to the triangle mesh generation baselines (e.g., MeshAnything, DeepMesh) may not be fair, due to the difference in training data and model capacity for long sequences (Fig 4).\n- The proposed model has less controllability over the ratio of triangles and quads in the output mesh, though a conditioning mechanism with a quad-dominance parameter is introduced.", "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": "DXSXC2gCoN", "reviewer": "Reviewer_N2bc", "summary": "The paper introduces QuadGPT, an end-to-end autoregressive framework for generating native mixed quadrilateral and triangular meshes directly from a point cloud input. QuadGPT proposes a unified serialization scheme that handles both triangle and quad faces using a padding-based tokenization, along with an Hourglass Transformer architecture for efficient processing of long sequences. Furthermore, the model is refined using a reinforcement learning approach, which uses a topological reward function to encourage the formation of clean, production-ready edge loops. The authors demonstrate that this approach significantly surpasses prior state-of-the-art methods in both geometric fidelity and topological coherence on a large, curated dataset.", "questions": "- The padding-based serialization seems pretty plain and inefficient for meshes with many triangles. Have the authors considered about using token compression techniques like BPT?\n- I wonder if the authors have tried experiments on openly available dataset? It's unfair to compare with other models trained on different datasets (of potentially worse quality). It's understandable to use proprietary datasets for the best performance, but the author should at least do some ablation study on the dataset quality, which can provide some insights for future research (e.g., the TripoSG paper).\n- How effective is the quad-dominance parameter for controlling the ratio of quad vs triangular faces? It also sounds pretty empirical to gradually anneal the data distribution with r from 0 to 1.\n- What's the insight for native quad mesh generation? Especially, I wonder its difference from first generating pure triangular meshes with the same model (e.g. use r=0), and then apply the proposed triangle-to-quad algorithm.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is clearly written and easy to follow. \n- The experimental results are impressive, demonstrating strong generalization capability on a wide range of meshes.", "weaknesses": "- The method itself is a combination of previous efforts (e.g., hourglass transformer and quad-dominance control form Meshtron, direct mesh tokenization from MeshXL, point cloud encoder from MeshAnything), with the biggest difference as the introduction of a mixed quad-triangle setting, which is a rather simple extension.\n- There is no ablation on the dataset. It's hard to tell if the performance boost is mainly from better data quality, and the comparison with previous works trained on public datasets is unfair. \n- Missing references and discussions on the triangle-to-quad conversion algorithm, for example, Blossom-Quad [1] and Blender's built in algorithm.\n\n[1] Blossom-Quad: A non-uniform quadrilateral mesh generator using a minimum-cost perfect-matching algorithm; Remacle, J‐F., et al.", "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)