Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning
Authors: Jian Liu
Jing Xu
Song Guo
Jing Li
Guojingfeng
Jiaao Yu
Haohan Weng
Biwen Lei
Xianghui Yang
Zhuo Chen
Fangqi Zhu
Tao Han
Chunchao Guo
Venue: NeurIPS 2025 spotlight ()
Keywords: Autoregressive Mesh Generation
Reinforcement Learning
Topology Optimization
Primary area: applications
TL;DR: We introduce Mesh-RFT, a novel framework leveraging localized preference optimization with topology-aware scoring to achieve state-of-the-art high-fidelity 3D mesh generation.

Abstract: Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present $\textbf{Mesh-RFT}$, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6\% and improves Topology Score (TS) by 3.8\% over pre-trained models, while outperforming global DPO methods with a 17.4\% HD reduction and 4.9\% TS gain. These results demonstrate Mesh-RFT’s ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation.

Mesh-RFT: Enhancing Mesh Generation via Fine-Grained Reinforcement Fine-Tuning
Jian Liu 1:- Jing Xu
Song Guo If
Jing Li2.3
Jingfeng Guo
Jiaao Yu²
Haohan Weng".4 Biwen Lei
Xianghui Yang"
Zhuo Chen
Fangqi Zhu
Tao Han' Chunchao Guo
Hong Kong University of Science and Technology Tencent Hunyuan 3 University of Science and Technology of China South China University of Technology
https://hitcslj.giin io/mesh-rft/
Mesh-RFT
FAST
FOOD
Figure Representative High-Fidelity Mesh Generation by Mesh-RFT. Gallery of meshes generated from point clouds, demonstrating intricate geometric detail and artist-like aesthetic quality.
Abstract
Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present Mesh-RFT, novel fine-grained reinforcement fine- tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into fine-grained RL strategy, Mcsh-RFT becomes the first method to optimize mesh quality at the granularity of individual
"Equal Contribution. Conresponding Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6% and improves Topology Score (TS) by 3.8% over pre-trained models, while outperforming global DPO methods with 17.4% HD reduction and 4.9% TS gain. These results demonstrate Mesh-RFT's ability to improve geometric integrity and topological regularity, achieving new statc-of-the-art performance in production- ready mesh generation.
Introduction
3D polygonal meshes serve as the foundational representation for digital assets in industries such as gaming, film, and product design. Despite their ubiquity, high- topologically optimized meshes-essentic for downstream tasks like editing, rigging, and animation-are are still predominantly handerafted by skilled artists. Recent advances in generative models have enabled automated mesh synthesis, significantly reducing the time and expertise required to produce production-reait 3D assets. This democratization of mesh generation broadens access to 3D content creation, empowering non-experts to produce geometrically precise and artistically viable models for applications ranging from immersive media to industrial design.
Existing 3D generative models often use intermediate representations like voxels [1, 21. point clouds [3, 4, 5], latent space [6. 7] implicit fields [8. 9J. While these avoid direct mesh generation complexitics, post-procsssing (c.g., Marching Cubes [10]) often introduces topological issues and smoothing. Native mesh generation [11] is more direct, with recent work using autoregressive models and neural compression (e.g.. VQ-VAE [12, 13, 14]) or geometric serialization tokenizers (e.g., [15, 16, 17, 18, 19]) for sequence- sequence-based generation. However, long sequences for high- resolution meshes can cause structural ambiguities and hallucinations (inconsistent edges, non- manifold vertices, distortions. holes), deviating from geometric constraints or artistic intent, ultimately leading to results that may not align with human aesthetic preferences or intended design. Though truncated training [20] helps, autoregressive methods still lack stable generation and high fidelity.
Recently, reinforcement learning [21, 22] has emerged as a compelling approach for aligning mesh generation more closely with human preferences. For example, DeepMesh [23] leverages Direct Preference Optimization (DPO) [24], a simple yet effective preference alignment technique that has also found utility in various other domains [25, 26, 27]. Nevertheless, directly applying reinforce- ment fine-tuning to mesh generation using this method encounters two primary challenges. Firstly, objectively quantifying mesh quality is difficult. DeepMesh relies manual annotation of preference pairs, which is expensive, time-consuming, introduces subjective bias, and limits the training data only 5,000 samples, hindering generalization. Secondly, its use of global reward signals fails to capture the local topological variations inherent in 3D meshes. As illustrated in Figure 2, high-quality and low-quality structures often coexist within a single mesh, leading to training noise due this mismatch in supervision.
To overcome these limitations, we introduce Mesh-RFT, a novel framework that combines Masked Direct Preference Optimization (M- DPO) with fine- fine-graine grained mesh quality evalua- tion for both global and localized refinement. Unlike prior work using subjective global re- wards as supervision signals [23]. we employ topology-awar scoring system with automated Settris--Bundaay Edge Ratio (BER) and Topol- ogy Score (TS)-to objectively evaluate mesh quality at both object and face levels, circum- venting the laborious manual annotation efforts. Mesh-RFT further employs a localized optimiza- tion mechanism utilizing M-DPO and quality- aware masks to specifically refine defective re- mesh. gions, thereby addressing the coarse supervision of global rewards. Extensive experiments across diverse meshes demonstrate Mesh-RFT's supe- rior performance, achieving significant improvements over both the pretrain baseline (24.6% HD
Figure 2: High-quality, artist-like structures often co- exist with messy, low-quality regions within the same

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reduction, 3.8% TS improvement) and global DPO (17.4% HD reduction, 4.9% TS improvement), establishing new benchmark for accuracy and fidelity in generative mesh modeling.
In summary, our contributions are as follows:
We introduce the first fine-grained reinforcement fine-tuning framework, that integrates Masked Direct Preference Optimization (M-DPO) with fine-grained mesh quality evaluation.
We devise an objective topology-awe aware scoring system for evaluating mesh quality, eliminating dependency on manual annotation and addressing subjectivity and scalability limitations.
We propose novel localized alignment mechanism that optimizes deficient regions geometrically and topologically via quality-aware aware masks, bridging the gap between global and local supervision.
Experiments demonstrate that our method achieves state-of-the-arr performance in high-fidelity 3D mesh generation.
2 Related work
2.1 3D Generation via Alternative Representations
Many 3D generative models avoid direct mesh modeling by using intermediate representations like voxels, point clouds, or implicit fields. Early voxel methods [1, 2] using grids faced memory issues. Point cloud methods [3, 4, 28, 29] with networks like PointNet [5, 30] struggle with consistency and detail. Implicit fields, especially neural fields [8. 9, 31, 32], offer efficient representations, These include score distillation with 2D diffusion models [33, 34, 35, 36, 37. 38] and 3D Transformer models like LRM [39, 40, 41, 42, 43], alongside recent latent diffusion methods [44, 45, 46, 47, 48, 49, 50, 51] that have demonstrated good scalability and performance. However, these approaches often rely on post-processing via Marching Cubes [10], which can cause topological issues, smoothing, and
artifacts.
2.2 Native Mesh Generation
While neural shape representations such implicit fields have been extensively studied, native mesh generation is an emerging area of research. Early approaches leveraging surface patches [52] or mesh graphs [53] often suffered from quality limitations. Diffusion- based methods [54, 55] have seen limited exploration in this domain, potentially due to inherent difficulties in directly processing meshes. PolyGen [11] demonstrated promise by autoregressively generating mesh vertices and faces. MeshGPT [12] encoded meshes into quantized tokens using VQ-VAE [56] for autoregressive generation. Subsequently, MeshXL [15] proposed a one-stage autoregressive model operating on coordinate-level mesh sequences. Various tokenization techniques [16, 17, 19, 57] and efficient training strategies [20, 58] have been explored to address the challenges of long sequences in high- resolution generation; however, achieving stable and high-fidelity results remains significant hurdle.
Reinforcement Learning for Mesh Generation
Reinforcement Learning (RL) [59] has gained traction for 3D generation [60] using human feedback. Reinforcement Learning from Human Feedback (RLHF) aligns models with preferences by training a reward model, then fine-tuning with RL. However, RL.F is costly and unstable for 3D) tasks. Direct Preference Optimization (DPO) [24] offers a more efficient, stable alternative by removing the reward model. Despite success in language and image domains [25, 26], DPO's application 3D meshes limited, Closely related, DeepMesh [23] uses global rewards for alignment but struggles with 3D mesh heterogeneity, over-optimizing some regions and under--ptimizing others. Thus, RL methods addressing local mesh structures are crucial for better 3D mesh quality and consistency.
Method
This section details the Mesh-RFT framework. As illustrated in Figure 3, our pipeline consists of three stages: First, supervised pretraining is performed by feeding point clouds and ground truth

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Stage2: Preference Datoset Construction
Generate
Trai
Score System
Pretrained Model
domple
DER(M)) BERIM)) A
TSM,)>> TSIM;)
Trput much
Nernal
StageI: Mesh Generation Pre-training
Hourglass AutoRegressive Transformer
Proference
Encoder
Stage3: Mesh Generation Post-training
Reference Network
Mask DPO
Policy Network
( EE, www.
[ (LL M, (L (P.M))
Figure 3: Mesh-RFT Framework Overview. The pipeline comprises three stages: 1) Mesh Generation Pre-training using an Hourglass AutoRegressive Transformer and Shape Encoder; 2) Preference Dataset Construction where pretrained model generates candidate meshes, and awware
topology-awer aware score system establishes preference pairs; and 3) Mesh Generation Post-training which employs Mask DPO with reference and policy networks for subsequent refinement.
mesh sequences into the model. Second, the pretrained model generates candidates, and a topology- aware score system builds preference dataset. Third, lopology-awand Masked Direct Preference Optimization is applied to post-train the model using this preference dataset refine its performance.
3.1 Mesh Generation Pre-training
Firstly, we discuss mesh tokenization. Prior works [16, 17, 19] compress mesh sequences manage sequence growth with increasing faces, but such techniques embed excessive geometric information per token, causing cascading face errors when single token is incorrect (e.g.. BPT [19] often introduces patch-level holes). To avoid these issues, we adopt the uncompressed mesh sequence method introduced from MeshXL [15]. Specifically, for given mesh M, we first quantize the vertex coordinates of each face, and then flatten them in XY Z order to construct complete token sequence.
Model Architecture. To better capture the structure of the mesh, rather than framing mesh genera- tion generic sequence task, we utilize Hourglass Transformer architecture [20, 61]. Our model processes inputs hierarchically and incorporates two shorten and two upsample operations. The shorten operations reduce the token sequence length using techniques such as linear or attention-basee pooling, while the upsample operations expand the sequence back to its original length through linear or attention- based methods. This design enables the model to efficiently capture both high-level patterns and fine-gn fine-grained details. In point-cloud conditioned mesh generation, achieving fine-gra fine-grained and complex structures requires not only a powerful decoder but also high-quality point cloud features. To this end, adopt the point cloud encoder pretrained in Hunyuan3D 2.0 [48] to do this. These features are injected into our autoregressive decoder as keys and values via cross-attention [62].
Truncated Training and Sliding- Window Inference. To reduce memory and computational costs, we employ truncated training with fixed-length segments. This approach involves extracting smaller, fixed-length segments from the mesh sequence for training, rather than using the entire sequence. When segment does not contain the start-of sffrt-of-seqeence (SOS) token, we pad small prefix portion to avoid misleading the model. During inference, we use sliding window approach to enhance both speed and generation quality. The sliding process begins once 40% of the training window size is covered, and only the most recent 30% of tokens are retained. This method reduces computational load by focusing the most relevant tokens, distant tokens typically have less influence on each

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good
bad
good
bad
good
bod
BER=O HD =00996
BER=0.0025 =65.3
BER=O 0016 TS :804
BER=0.0020 TS HD =01453
BER=O. BER=0.0001 0001
BER=0 HD =01457
=797
HD =0.1016
HD =0.375
HD =01238
Examples of collected preference pairs. Meshes are annotated as preferred using our scoring system. For certain pairs, the selected "good" meshes may exhibit inferior local performance in specific regions compared to the rejected "bad" meshes.
Figure 4:
other. Additionally, it helps mitigate high perplexity at the tail of each window, leading to more accurate and efficient generation.
3.2 Preference Dataset Construction
We establish a systematic pipeline for constructing the preference dataset, which is used for RLHF fine-tuning in the second stage. This pipeline consists of three key components: candidate generation, multi-metric evaluation, and preference ranking. The process is described as follows.
Candidate Generation. For each input point cloud P, generate eight candidate meshes (M..Mp.... ".. .M&. using the pre- pre-trained model G
Multi-Metric Evaluation. We evaluate each candidate mesh using a comprehensive set of criteria assess both geometric consistency and topological quality. In addition to measuring the geometric alignment with the input data, we introduce two topology-oriented metrics that specifically aim to capture the structural integrity and coherence of the generated meshes. These three metrics are: Boundary Edge Ratio (BER) and Topology Score (TS) for evaluating topology, and Hausdorff' Distance (HD) for evaluating geometric consistency.
Boundary Edge Ratio (BER): This metric, defined as BER(M) Eam, quantifies the integrity E.M
of the mesh by calculating the proportion of its boundary edges (EaM) the total number of edges (F.)) Boundary edges are those connected to only one face, and a high BER value (typically above 0.002 in our dataset, which consists mostly of closed meshes) suggests potential issues like surface discontinuities, holes, or mesh damage. Ideally, a closed, manifold mesh should have BER of O.
Topology Score (TS): The Topology Score, (S(M)= = E W;Se( (;s((Q(MM) ((M)), assesses the structural quality of a mesh M by analyzing a derived quadrilateral mesh ((M),, obtained through standard riangle-to-quad merging. The score is weighted which measures the efficiency of the conversion; Angle Quality (w2 = 0.2), quantifying the deviation of quadrilateral angles from 90%; Aspect Ratio (w3 0.3), evaluating the regularity of quadrilateral shapes; and Adjacent Consistency (w04 = 0.1), encouraging uniform aspect ratios between neighboring quadrilaterals. This quadrilateral--basee meshes are preferred in industrial applications, making the quality of the quadrangulation a practical indicator of the topological soundness of the original triangular mesh. Further details are in the supplementary material A.3.
of four sub-metrics: Quad Ratio (wi 0.4),
evaluation is used because quad
Hausdorff Distanee (HD): This standard metric measures the maximum distance from a point in one set to the closest point in the other set. Here, it quantifies the geometric alignment between the
5

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reconstructed mesh Mp and the input point cloud P by measuring the maximum distance between their respective point samples. lower HD value indicates better geometric reconstruction.
Preference Ranking. To construct the preference dataset, we generate pairwise comparisons through exhaustive combinations of the eight candidate meshes for each input point cloud P. resulting in total of (2) = 28 pairs, For each pair (Mip, M2),, we define preference relation Mp > M if and only if Mip outperforms Mip across all three evaluation metrics:
BER(M)) < BER(M)) A TS(M)) > TS(M)) HD(Ms) <DD(M))
Mp M³ =
(I)
We refer to M the positive sample (denoted M) and Mr the negative sample (denoted M,p) for the pair. Using this rule, construct set of preference triplets of the form (P, P. which constitutes our preference dataset for reinforcement learning with human feedback.
3.3 Mesh Generation Post-training
While our pre-trained model produces topologically valid meshes, persistent challenges remain: (1) localized geometric imperfections in high-curvatrre regions, and (2) inconsistent face density distribution causing aesthetic artifacts. Although DeepMesh [23] adopts RLHF for mesh refinement, its reward function is primarily based on global mesh structure, making it insufficient for unn-grained control over local mesh quality. To address these limitations, we propose Masked Direct Preference Optimization (M-DPO)- spatially aware extension of DPO) [24]. M-DPO introduces quality localization masks to guide learning toward problematic regions, enabling more targeted and effective mesh refinement.
Quality-Aware Local Masking. The goal of local masking is to differentiate high-quality regions of a mesh from those of lower quality. Given triangular mesh M, assess each triangle face individually. A face is labeled as good if it satisfies the following two conditions: (1) it can be successfully merged into quadrilateral, and (2) the resulting quad has a quality score above predefined threshold. The quad quality is evaluated using weighted combination of three metrics introduced in Section 3.2: Angle Quality, Aspect Ratio, and Adjacent Consistency. For each triangle face labeled as good, we assign value of to all corresponding token positions in the mesh sequence (typically tokens per face). Conversely, faces that do not meet the criteria are considered bad, and their associated tokens are assigned a value of O. We define the local masking function as é, such that ((M) (0, 1) MIM, where [M denotes the length of the token sequence representing mesh M.
Masked Direct Preference Optimization. Standard DPO tends to optimize global reward signals uniformly across the entire mesh sequence, which can lead to over-smoothed results and the loss of fine-grad fine-grained geometric details. In contrast, Masked Direct Preference Optimization (M-DPO) addresses this limitation by applying element- wise importance weighting guided by local quality masks, allowing the model to focus refinement specifically low-quality regions. As illustrated in Figure 3, we designate the pretrained model from the first stage as the reference model, denoted frozen during training. A trainable policy model Gp is
G := G whose parameters then initialized with the parameters of G and subsequently fine-tuned to better align with human preferences by encouraging it to generate outputs closer to the positive examples in our preference dataset. The objective of M-DPO is to maximize the likelihood of preferred (positive) samples over less-preferred (negative) ones, with a focus on quality-crite regions identified via local masks:
(M-DPO(TT: Teet) = -EPP log (BC (BC - 3C (0C((,MM))
(2)

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Artist Mesh
Point Cloud
MeshAnythingv2
BPT
DeepMesh
Ours
Figure 5: Qualitative comparison artist-desinned meshes. Our method generates coherent and visually plausible surfaces with finer structural details and fewer topological artifacts compared to baseline approaches.
where the positive and negative log-ratio terms computed
[ PP) O @(M
2 (+, =
log
(MPl)) 6(M [Mp())))) (1-)(M5) lli
(3)
(P, = log
[MM[))) @ @ ((M))))) II
Here, denotes the preference dataset, and π is the token-level probability distribution produced by the model. The operator @ indicates element-wise (Hadamard) multiplication, and ||- denotes the E norm over the token sequence. The hyperparameter 3 controls the sharpness of preference separation, and a is the standard sigmoid function. M-DPO effectively preserves satisfactory regions while actively refining low-quality areas identified by the local quality mask. This targeted optimization strategy not only maintains the global structure but also enhances local geometric fidelity, offering finer control over mesh generation quality compared to standard DPO.
4 Experiments
4.1 Experiment Settings
Datasets Our model pretrained on 2M meshes from large-scale datasets including ShapeNetV2 [63], 3D-FUTURE [64], Objaverse [65], Objaverse-XL [66], and licensed assets. After filtering low-quality scans and poorly topologized CAD models, 800K meshes form the fine-tuning subset. For preference alignment, we construct a specialized dataset of 10,000 generated meshes, each paired with topological variations derived from the same input point cloud. To enhance geometric generalization, meshes are perturbed at the vertex level and subsampled from an initial 50K-point cloud to 16,384 points, without enforcing watertightness. For evaluation, we employ two test sets: (1) 100 high-quality, artist-designed meshes for qualitative analysis, and (2) 100 dense, out-of-distribution meshes generated by Hunyuan2.5 [48], providing rigorous real- world validation. More data details can be seen in Supplementary A.I.
7

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Dense Mesh
Point Cloud
MeshAnythingy2
BPT
DeepMesh
Ours
Figure 6: Generalization results on dense, out-of-distribution meshes. Our model demonstrates superior geometrie fidelity and surface continuity, maintaining high-quality reconstruction even under complex and unseen input conditions.
Implementation Details We pretrained on 256 NVIDIA H20 GPUs (2/GPU) for 10 days with AdanW [67] (3, 0.9, B2 0.99) and Flash Attention, following 100-step linear warm-up. M-DPO post-training took hours on 64 GPUs with 5e learning rate. See supplementary material A.2 for full details.
Baselines. We benchmark our approach against leading mesh generation methods, including MeshAnything [16]. BPT [19], and DeepMesh [23]. Since DeepMesh only publicly provides inference code and 512M parameter version, we use this configuration for comparison.
4.2 Qualitative Results
We qualitatively compare our method with existing baselines. As shown Figure our model produces meshes that are significantly more coherent, artistically plausible, and faithful to the input geometry, particularly in challenging regions such as fine-grainet structures and curved surfaces. These results highlight In contrast, baseline methods often exhibit structural artifacts such as incomplete regions, broken connectivity, excessive smoothing, especially in geometrically intricate areas. To further evaluate generalization beyond the training distribution, we conduct experiments on set of dense, high- resolution meshes not during training. As illustrated in Figure 6, method consistently outperforms prior approaches in reconstructing complex geometry and maintaining surface continuity under high-resolution inputs. These results demonstrate that our model only performs well on curated artistic data but also generalizes effectively to challenging, real-world examples.
model's ability to preserve detail and maintain topological regularity.
4.3 Quantitative Results
Table presents quantitative comparison of our method against baselines on artist-designed meshes and dense meshes derived from Al-generated representations. We report both geometric and topological metrics, including Hausdorff Distance (HD), Topology Score (TS), and Boundary Error Rate (BER). Our method consistently outperforms competing approaches across all metries, demonstrating superior geometric fidelity and topological coherence. To further validate perceptual quality, we conducted user sUUd which participants were asked to compare mesh outputs based visual plausibility and structural integrity. The results indicate strong preference for our

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Point Cloud
DPO
Mask DPO
Figure 7: The effectiveness of Mask DPO. The addition of Mask DPO enhances the visual fidelity of the generated meshes, despite similar geometric performance across methods.
Table Quantitative comparison with other baselines in Artist and Dense Meshes. Our approach achieves superior performance in both geometric accuracy and visual fidelity compared to existing baselines. DeepMesh" were tested using their 0.5 B version.
Data Type
Artist Meshes
Dense Meshes
Metrics
CD 4
HD 4
TS f
BER 4 US 1|
CD 4
HD 4
TS f
BER +
US f
Meshhnnythngg2 [16]
0.2143 0.4197
68.3 0.0749
9%
0.2265
0.4760
72.0 0.0913
8%
BPT [19]
0.1275 0.2735
72.7 0.0280
20%
0.1615
0.3347
73.7
0.0113
18%
DeepMesh" [23]
0.1331
0.2866
74.9 0.0296
22%
0.1760
0.3570
75.8
0.0044
20%
Ours
0.0973 0.1826
77.5
0.0182
45%
0.1286
0.2411
79.4
0.0015
40%
method, confirming that its advantages are not only quantitatively measurable but also perceptually significant.
4.4
Ablation Study
4.4.1
Score System
We evaluate the efficacy of our score-based preference system within the domain dense mesh generation. As demonstrated Table 2, employing only Hausdorff Dis- tance to differentiate between high- and low-quality meshes (denoted as N-DPO) yields marginal improvements in geomet- ric consistency over the pretrained model (Pretrain) and exhibits decrease in the TS score. Conversely, leveraging our proposed composite scoring system (denoted as S-DPO) for the construction of preference data facilitates substantial performance gain.
Table 2: Quantitative Evaluation of Score System and Mask DPO Methods.
Method
CD
HD
TS
BER
US
Pretrain
0.1588
0.3196
76.5
0.0033
30%
N-DPO
0.1455
0.2919
75.7
0.0028
32%
S-DPO
0,1348
0.2625
77.9
0.0023
35%
M-DPO 0.1286
0,2411
79.4
0.0015
40%
4.4.2 Mask DPO
Figure illustrates that standard global DPO often fails to capture local variations in mesh quality. Our proposed topology-aware local mask mechanism effectively addresses this limitation by enabling the model to learn from spatially localized preference signals. Built on the preference dataset derived from our scoring system, the Mask-DPO model (denoted as M-DPO) demonstrates clear advantage over the global score- score-based DPO baseline (S-DPO), as shown in Figure 7. This localized learning strategy leads to significant improvements both quantitative metrics and human preference, as confirmed in Table 2. Notably, M-DPO produces outputs that are not only closer to the ground truth but also more consistently favored by human evaluators, providing strong empirical support for localized preference learning.

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Conclusion
Generating high-quality 3D meshes remains a significant challenge. We introduced Mesh-RFT, a novel framework employing topology-aware scoring and Masked Direct Preference Optimization (M-DPO) for fine-grained refinement. By leveraging objective metrics and localized optimization, Mesh-RFT advances the state-of-the-ee in automated mesh generation. Our approach significantly improves both the geometric accuracy and topological fidelity of generated meshes compared to previous methods. This work offers substantial step forward in creating production-ready 3D assets for a wide range of applications. Limitations and future work are discussed in appendix C.
Acknowledgements
This research was supported by fundings from the Hong Kong RGC General Research Fund (152244/21E, 152169/22E, 152228/23E, 162161/24E). Research Impact Fund (No. R5011-23F 1-23F, No. R5060-19), Collaborative Research Fund (No. C1042-23GF), NSFC/RGC Collaborative Re- search Scheme (No. CRS HKUST602/22)) Areas of Excellence Scheme (No. AoE/E-601/22-R), and the InnoHK (HKGAI).
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If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks. mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of MI.).
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Appendix
A More Implementation Details
A.1 Data Details
Bad
Good
Figure 8: Examples of Wiring Complexity in the Dataset. The dataset contains cases with high- quality surface triangulations alongside instances where local regions exhibit lower quality.
After filtering low-quality scans and poorly topologized CAD models, dataset size was reduced from 2 million to approximately 800,000 samples, with an average face count of 5,000. The distribution of face counts in this refined dataset is illustrated Figure 9, Despite this initial filtering, as demonstrated in Figure 8, the dataset still includes instances where local surface triangulation quality is suboptimal. These instances are challenging to entirely eliminate due the fact that even within lower-quality cases, regions with good topology often exist.
A.2 More Training and Inference Details
Our model consists of 24 Transformer layers (1.1B parameters) arranged a three-stage hour- glass (incturre2-4-12-4-0 It features a hidden dimension of 1536 and 16 attention heads. The vocabulary size for vertex coordinate quantiza- tion is 1024. The architecture supports 36,864- token context window during inference and gen- erates meshes through temperature-controlleed sampling (T' 0.5), balancing output diversity and stability. For the pretraining phase, we ini- tially trained on 2M meshes for days, followed by an additional 4 days of training on filtered 800k meshes. A 5k 5k-face mesh from the preference dataset requires approximately 45k tokens. Generating 80,000 meshes from 10,000 dense meshes took about 2 days, with process- ing handled by 64 GPUs batch size of per GPU, resulting in speed of around 40 tokens/s.
ristribution of Face Count Dalavet
20000
15030
10020
107
207
309
Figure 9: Face Count Distribution the Fine-tuning Dataset. This figure presents the distribution of face counts within our fine-tuning dataset, which comprises approximately 800k samples with average faces per model.
23

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In contrast, calculating the EDR, TS, HD, and local mask for each mesh was completed in under hour on single machine. Furthermore, we utilize ZeRO-2 to minimize GPU memory consumption.
A.3 Metries Details
The Topology Score (SM provides quantitative measure of the structural quality of a mesh M. computed based on the properties of derived quadrilateral mesh (M and is defined weighted linear combination of four sub-metrics:
TS( = si (2((M) + w2 s2 s²(2(M)) + wg 53 s3(2(M)) + w4 sx(2(M)) (4)
where the weights are empirically set to w1 = 0.4 (Quad Ratio), W2 = 0.2 (Angle Quality), wy = 0.3 (Aspect Ratio), and W4 = 0.1 (Adjacent Consistency), satisfying Σ Wt 1. The sub-metrics are formally defined as follows:
Quad Ratio ($1): This metric assesses the efficiency of the triangle-to-uee toauud conversion. Let Fo be the set quadrilateral faces and FT be the set triangular faces ((M). The Quad Ratio is given by:
Fo
(Q(M))
[F Fel
where denotes the cardinality of the set.
Angle Quality (s2): This metric quantifies the deviation of quadrilateral angles from the ideal 90%.
For each quadrilateral q E Q(M), let A(q) = {af, al, ag. ag) be the of its internal angles. The Angle Quality is defined as the average normalized deviation:
E.e A(g) la - 90%
82 (2(M)) = ((M)
Σ TEQ(M)
(6)
360
Aspect Ratio (s3): This metric evaluates the regularity of the quadrilateral shapes. For a quadrilat- eral q E Q(M) with side lengths 1g,1, 1, 14.3, 14,4, 4, the aspect ratio rg defined as:
"max (l, 4.a))
max(Ly
max
min 4,1, 4.33) min 14,4)
An additional edge ratio for each quadrilateral is computed as the average of its side lengths normalized by the maximum side length:
l
(8)
max
The Aspect Ratio sub-metric 83 is then a combination of these measures:
(1eim))
ss(2(M)) 0.5 ( ExEO(M)
+
Σ DE ((M(M)
Gg
A Adjacent Consistency (84): This metric encourages smooth variations the aspect ratios of neighboring quadrilaterals. For quadrilateral 4 E Q(M), let A(g;) be the set of its adjacent quadrilaterals, and let ra be the aspect ratio a neighboring quadrilateral Oj E (((g)) (calculated in Equation 7). The average aspect ratio difference for qi is:
dg. = W(()))
Σ ()E)(()))
Ir rgy
(10)
The Adjacent Consistency sub-metric 84 is then defined as the average of a consistency score based on this difference over all quadrilaterals:
(2((M) = Q(M)
Σ
(11)
I+
vEQ(M)
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B
More Results
We present further comparative results in Figure 10 and Figure 11, respectively. MeshAny- thingV2 [16], due to its Adjacent tokenizer, frequently exhibits line-shaped discontinuities. BPT [19], employing a block patch-based tokenizer, is prone generating patch-level holes. DeepMesh [23] 512M version demonstrates significant instability. While exhibiting better topological visual quality, likely due the use of truncated training and global-reward DPO, it generates excessively dense meshes lacking the adaptive tessellation characteristic of artist- artist-desinnd meshes. Our method, which incorporates M-DPO, achieves superior visual quality and mesh tessellation.
Artist Mesh
Point Cloud
thiinny
DeepMesh
Figure 10: Comparative Results for Mesh-RFT and Baseline Methods on Artist-Designed Meshes.
Dense Mesh
Point Cloud
MeshAnythingy2
BPT
DeepMesh
Ours
Figure 11: Comparative Results for Mesh-RFT and Baseline Methods on Al-Generated Dense Meshes.
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C Limitations and Future Work
Computational Efficiency While Mesh-RFT demonstrates significant advancements in mesh generation, its computational efficiency warrants further investigation. Exploring engineering opti- mizations, potentially drawing inspiration from efficient inference techniques such as vLLM [68] employed in large language models, could lead to substantial accelerations.
Topological Correctness in Complex Geometries Ensuring robust topological correctness, partic- ularly for intricate object geometries, necessitates continued research. As depicted in Figure 12, our model can exhibit topological defects such as holes in complex geometric scenarios. This may stem from limitations in the representational capacity of the point cloud encoder to capture fine-grained details within these complex structures. Future directions could involve leveraging more powerful, pre-trained point cloud encoders, increasing the number of tokens utilized, and scaling the decoder parameters to enhance the model's ability to discern intricate geometric features.
Conditioning Modality As illustrated in Figure 12, dense meshes generated by Hunyuan2.0 [48] can sometimes exhibit a loss of fine details. Furthermore, conditioning on point clouds sampled from watertight dense meshes may exacerbate this information loss. Future work could explore alternative conditioning strategies, potentially bypassing the intermediate dense mesh representation and directly generating artist-quality meshes from image inputs (image-to-mi generation).
Topology Reward Refinement The current reward function is relatively basic. Future research could focus exploring more generalized and sophisticated topology rewards, as well integrating real-time, state-of-the-ar reinforcement learning strategies [69, 70].
Addressing these limitations will be crucial for broadening the applicability and enhancing the robustness of Mesh-RFT across a wider range of diverse and challenging 3D modeling tasks.
Image
Dense Mesh
Point Cloud
Ours
Figure 12: Limitations of Mesh-RFT. Examples showcasing potential topological defects (holes) in complex geometries and loss of fine details in generated meshes.
26


Reviews:
[{"review_id": "GJ4fMvnElI", "reviewer": "Reviewer_cRc7", "summary": "The paper proposes to use several mesh topology metrics (BER, TS, HD) to guided DPO fine-tuning for point cloud to mesh generation. The approach begins by pretraining a point cloud to mesh model. A preference dataset is then constructed by generating multiple mesh variants from point clouds sampled from the same input mesh. Using the three metrics, positive and negative samples are constructed. The model is then fine-tuned with the preference dataset using DPO to improve generation quality. In particular, a novel masked DPO strategy is adopted to focus only on regions of the mesh with good topology. The method helps improve local structural quality and avoids the need for human annotation required by previous work.", "questions": "1. The comparison against DeepMesh isn't entirely fair as the backbone used in the paper requires more GPUs and days to train. This makes it hard to isolate the effects of the stronger backbone from the proposed masked DPO fine-tuning. For example, in figure 5 and 6 a lot of failure cases for DeepMesh involve incomplete meshes, likely due to the weaker backbone. A fairer comparison would involve either fine-tuning the DeepMesh backbone with the paper's method or using the stronger backbone with DeepMesh's DPO training.\n\n2. I would have liked to see more qualitative results for ablating each of the different metrics used for DPO tuning to better understand their contributions in mesh quality.\n\n3. As mesh generation models get better at generating meshes with \"better topology\". It raises a question of how substantial these gains are in actual downstream applications. For instance, for row 2 of figure 5, both the DeepMesh and proposed result look to have good topology. It is hard to differentiate which one is better. While good topology is important for tasks like facial animations as bad topology can cause rendering artifacts during deformations of the face. The current quantitative and qualitative results don't clearly show whether these improvements translate to practical benefits. It would improve the paper if authors can include downstream application results or at a minimum a discussion on the limitations of current evaluation metrics and how they can be improved to capture these application related qualities in the future.\n\n4. Related to points 1 and 3, the user study may favor the proposed method simply because its stronger backbone produces more complete meshes. This raises questions about what users in the study are actually judging, and whether non-expert users can reliably distinguish between good and poor mesh topology.\n\nFor questions 1 and 2, I hope the authors can include additional experiments to address these points. As for points 3 and 4, I understand it might not be feasible to conduct these studies. In that case, I am fine with additional discussions of these issues and thoughts on how to design better evaluation metrics in the future to reflect downstream application gains. If these points can be adequately addressed I am willing to increase my rating.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\nThe paper is clearly written and easy to follow. The details for each component of the model is mostly covered. I appreciate the inclusion of the ablation of different reward configurations in table 2 for better understanding the importance of each component. While the method is straightforward it offers better quantitative and qualitative results compared to previous works. \n\n## Weaknesses\n\nI believe the evaluation aspects of the paper would benefit from additional experiments or further discussion:\n\n1. Comparison against DeepMesh isn't entirely fair\n2. Lack of qualitative figures showing the effects of how each metric contributes to mesh topology\n3. Lacking more concrete examples of how these meshes with better topology can improve downstream applications\n4. On the validity of the user studies\n\nI have listed more specific details in the questions below.", "was_revised": true, "final_justification": "The authors have addressed my concerns in the rebuttal. And I believe the authors will be able to incorporate the rebuttal results in the revised version of the paper.", "rebuttal": "We appreciate your insightful feedback. We address your concerns below.\n\n**1. On the Fairness of Comparison Against DeepMesh**\n\n**Response:** We agree the most critical comparison is between our proposed M-DPO and a standard global DPO when applied to the *same* strong backbone. As shown in our paper's Table 2, our method already outperforms a rule-based global DPO (S-DPO). To further demonstrate this, we conducted an additional ablation study including a global DPO trained on human preference pairs (H-DPO). The results below show that our M-DPO is superior to both global DPO variants. Interestingly, the rule-based S-DPO slightly outperforms the H-DPO, likely because our automated scoring provides more consistent preference signals than subjective human ratings.\n\n| Method Description | HD (↓) | TS (↑) | BER (↓) |\n| :--- | :--- | :--- | :--- |\n| H-DPO Global DPO (Human Pref.) | 0.2764 | 77.1 | 0.0020 |\n| S-DPO Global DPO (Rule-based Pref.) | 0.2625 | 77.9 | 0.0023 |\n| **M-DPO Our Method (Masked DPO)** | **0.2411** | **79.4** | **0.0015** |\n\n**2. On Lacking Qualitative Analysis for Metric Contributions**\n\n**Response:** While we cannot add new figures during the rebuttal, the quantitative ablation study below demonstrates the contribution of each component. As shown, DPO effectively learns the specified preference rules. Introducing the HD metric improves geometric consistency. Further incorporating our topological scores enhances mesh integrity by reducing holes and improving quad-convertibility. Finally, our full M-DPO method leverages local masking to refine fine-grained details, achieving the best overall performance. For a more intuitive understanding of how different topological scores impact mesh quality, please refer to our response to Reviewer dibn, Question 1, where we provide a detailed breakdown.\n\n| Method Configuration | HD (↓) | TS (↑) | BER (↓) |\n| :--- | :--- | :--- | :--- |\n| Pre-trained Baseline | 0.3196 | 76.5 | 0.0033 |\n| + DPO (HD only) | 0.2919 | 75.7 | 0.0028 |\n| + S-DPO (Global DPO) | 0.2625 | 77.9 | 0.0023 |\n| **+ M-DPO (Full Method)** | **0.2411** | **79.4** | **0.0015** |\n\n**3. On Lacking Examples for Downstream Applications**\n\n**Response:** We agree that illustrating downstream impact is crucial. While a full pipeline is beyond this paper's scope, clean, quad-dominant topology is the industry standard for 3D artists because it saves significant manual effort and is essential for stable rigging and skinning, preventing artifacts like \"skin tearing\" during animation. Our method generates assets that are closer to this production-ready standard, directly addressing issues seen in baselines (e.g., the damaged foot in DeepMesh's result in Figure 5, row 2). To make this tangible, we commit to adding a dedicated section in the appendix of our final version that qualitatively demonstrates these benefits by showing a side-by-side skeletal deformation comparison between a mesh with good topology and one with poor topology.\n\n**4. On the Validity of the User Study**\n\n**Response:** Mesh completeness is a critical aspect of quality, which is why we dedicated substantial computational resources to pre-training to ensure model stability. However, to isolate the perceptual benefits of our topological improvements alone, we conducted a targeted user study where participants were shown only cropped local regions from our results vs. DeepMesh and asked to rate the topology. The results confirm a strong user preference for our method's fine-grained quality, proving the gain is not just from better completeness.\n\n| Method | User Preference (Local Topology Only) |\n| :--- | :--- |\n| DeepMesh* | 21% |\n| **Ours** | **43%** |"}, {"review_id": "baTysRe9rl", "reviewer": "Reviewer_dibn", "summary": "This paper presents Mesh-RFT, a novel framework for high-fidelity 3D mesh generation that improves upon existing methods by introducing fine-grained reinforcement fine-tuning. The core innovation lies in the integration of a topology-aware, multi-metric scoring system with a new variant of Direct Preference Optimization, termed Masked DPO (M-DPO), which enables localized mesh refinement.\nThe framework operates in three stages: (1) supervised pretraining on large-scale mesh data, (2) automated construction of a preference dataset using Hausdorff Distance (HD), Boundary Edge Ratio (BER), and a custom Topology Score (TS), and (3) post-training with M-DPO that focuses on low-quality regions identified by a face-level mask. This local reward design addresses the limitations of previous RL-based methods that only use global mesh-level rewards.\nExtensive experiments on both synthetic and real-world data demonstrate that Mesh-RFT significantly improves geometric accuracy and topological regularity compared to strong baselines like DeepMesh, BPT, and MeshAnythingV2. The paper reports consistent gains in HD and TS, and the user study further confirms the perceptual quality of the generated meshes.", "questions": "1. The Topology Score (TS) is a core component of the preference scoring system, yet the paper lacks theoretical justification or ablation studies regarding the weight configuration of its four sub-metrics.\nI recommend conducting an ablation study to investigate how different weightings influence the ranking results and downstream mesh refinement performance. This would help validate the robustness and rationality of the scoring scheme.\n\n\n2. Although the local mask is a key mechanism in M-DPO, the paper does not provide an in-depth analysis of its impact on the training process.\nIt would be beneficial to include quantitative experiments (e.g., visualizing the evolution of high-quality regions before and after fine-tuning) to clarify how the mask affects model behavior and mesh quality refinement.\n\n\n3. Reinforcement learning, particularly under locally sparse reward settings, often suffers from training instability. However, the paper does not report any convergence curves or stability metrics.\nI suggest including training convergence plots or variance analyses across multiple runs to assess whether the training process is stable and consistent.", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\nThe paper introduces Mesh-RFT, a fine-grained reinforcement fine-tuning framework with a topology-aware scoring system and a novel Masked Direct Preference Optimization (M-DPO) approach. This combination leads to measurable improvements in both geometric accuracy and topological regularity of 3D meshes.\n﻿\nM-DPO extends traditional preference optimization by introducing spatial awareness via quality masks, addressing the challenge of reward sparsity and locality in 3D geometry tasks.\n﻿\nThe use of a multi-metric scoring system (HD, BER, TS) for automated preference dataset creation is well-motivated and scalable.\n﻿\nThe authors perform extensive evaluations across multiple datasets, including artist-designed meshes and dense meshes derived from AI-generated representations. The improvements are significant and robust.\n﻿\n﻿\n﻿\nWeaknesses\n\nThe proposed Topology Score (TS), a key component in the preference scoring system, is computed via a weighted sum of four sub-metrics (Quad Ratio, Angle Quality, Aspect Ratio, and Adjacent Consistency). However, the paper does not provide theoretical justification for the specific weight choices nor any ablation to validate their impact on overall performance or ranking robustness.\n﻿\nAlthough local quality masks are central to M-DPO, the paper lacks a detailed analysis of how these masks influence training dynamics. For example, it remains unclear whether the weighting of local masks may lead the model to unintentionally degrade high-quality regions; whether the model might overfit to the specific mask generation strategy, thereby compromising the preservation of global mesh structure; and whether using soft masks (i.e., continuous weights between 0 and 1) could be more effective than the current binary (0/1) masking approach for guiding fine-grained mesh refinement.\n﻿\nThe paper does not present the training convergence behavior or stability analysis of M-DPO. Given that reinforcement learning—especially under locally sparse reward settings—can be unstable, further investigation in this aspect is warranted.", "was_revised": true, "final_justification": "The authors have carefully addressed the questions I raised. My concerns regarding TS, the local mask, and instability have been adequately resolved.", "rebuttal": "We appreciate your insightful feedback. We address your concerns below.\n\n**1. On the Justification of Topology Score (TS) Weights**\n\n**Response:** Our TS weights were optimized through iterative industrial benchmarks. To provide empirical support, our ablation study on 100 Hunyuan2.5 meshes shows that weight adjustments primarily influence local topological characteristics, not global geometry (HD remains stable, with Δ < 0.002).\n\n| Weight Config (w1/w2/w3/w4) | HD (↓) | BER (↓) | US (↑) | Topology Characteristics |\n| :--- | :--- | :--- | :--- | :--- |\n| Default (0.4/0.2/0.3/0.1) | 0.2411 | 0.0015 | 40% | Balanced squares/diamonds |\n| Equal Weights (0.25 each) | 0.2402 | 0.0018 | 37% | More triangles (merging failures) |\n| High w₂ (0.4/0.3/0.2/0.1) | 0.2420 | 0.0016 | 42% | Rectangles dominant |\n\nThis analysis reveals three key findings:\n1.  **Weight sensitivity is local and topological:** Adjustments alter face-type distribution without significantly affecting the global shape.\n2.  **Equal weights impair quad merging:** This configuration often results in non-coplanar triangle pairs, hindering quad conversion.\n3.  **Higher w₂ improves aesthetics and utility:** It boosts user preference by producing more rectangular quads, which streamline UV unwrapping workflows.\n\nThank you for highlighting this nuance. Based on these findings, we will adopt the **High w₂** configuration in our final model to optimize for both user preference and practical utility.\n\n**2. On the In-Depth Analysis of the Local Mask**\n\n**Response:** We appreciate the suggestion for a deeper analysis of our M-DPO mechanism. We address your specific concerns below:\n1.  **On Degrading High-Quality Regions:** Our M-DPO framework is explicitly designed to prevent this degradation by selectively applying the preference loss. As defined in Equation 3, the optimization has two distinct targets: it **reinforces the high-quality regions** of the preferred mesh (by applying the mask φ(M+)) while simultaneously **penalizing the low-quality regions** of the rejected mesh (by applying the inverted mask 1−φ(M-)). The flawed parts of the winner and the high-quality parts of the loser are both excluded from the loss calculation. Therefore, existing high-quality structures are not only preserved but are actively rewarded, ensuring that fine-tuning consistently leads to a net improvement in mesh quality.\n2.  **On Overfitting to the Masking Strategy:** Our extensive experiments show no evidence that the model overfits to the specific mask generation strategy. Because the model is trained to recognize and generate good topology (via the positive sample term) while unlearning bad topology (via the negative sample term), it learns the underlying principles of topological quality. This generalizes well to unseen mesh configurations, and the global structure is preserved because the fine-tuning process is guided by a robust pre-trained reference model.\n3.  **On Soft vs. Hard Masks:** We investigated the efficacy of soft masks versus our binary (hard) approach. For this experiment, we generated soft masks by normalizing each face's topology score into a continuous weight (e.g., via a sigmoid function centered at our quality threshold of 70). As shown below, the hard mask provides superior performance and faster convergence. We hypothesize this is because a binary mask offers a clear, unambiguous signal to the model—\"reinforce this good region\" or \"penalize this bad region\"—whereas a soft mask can introduce noisy gradients from moderately-scored regions, slowing down the learning process.\n\n| Mask Type | HD (↓) | TS (↑) | Convergence Hours |\n| :--- | :--- | :--- | :--- |\n| **Hard Mask (Ours)** | **0.2411** | **79.4** | **Baseline (8 Hour)** |\n| Soft Mask | 0.2489 | 78.9 | ~50% Slower (12 Hour) |\n\n**3. On Training Stability and Convergence**\n\n**Response:** We thank you for this valid concern. After initial hyperparameter exploration, we have achieved a stable and reproducible training configuration. The key adjustments were:\n1.  reducing the learning rate to `5e-7` (significantly lower than the pre-training rate of `1e-4`),\n2.  increasing the DPO beta to `0.5` to stay close to the robust reference model,\n3.  using a dataset of 10k preference pairs, which we found sufficient for convergence without overfitting, and applying a label smoothing rate of `0.3` to the DPO loss.\n\nWith these settings (on 64 GPUs, total batch size 64), the training loss remains stable at ~0.693 for the first 2k steps, then steadily decreases to ~0.6 by the 4k step mark. Concurrently, the reward margin (`reward_chosen` - `reward_rejected`) begins to climb from 0 to ~0.7 after 2k steps, indicating successful and stable policy optimization."}, {"review_id": "2rctHLCSNq", "reviewer": "Reviewer_ynVT", "summary": "The paper proposes a method for mesh generation with artist-created topology, building upon the autoregressive generation line of works. The method mainly uses the architecture and mesh tokenization strategy of Meshtron and introduces several novelties to improve the generation quality: 1). Masked Direct Preference Optimization (M-DPO), a fine-grained reinforcement (RL) fine-tuning framework, 2). Topology-aware scoring system that can be effectively integrated into the fine-grained RL finetuning, eliminating the need of human preference annotation.", "questions": "1. I think the dense meshes from here: https://microsoft.github.io/TRELLIS/ cover from simple to highly complex shapes. I would appreciate if the authors can show the results from those meshes without cherry-picking. I would want to see the results from baselines models as well as all the models from the ablation study. Imperfect results are understandable. Convincing results can increase my final rating. For all models, run a same number of generations with different seeds (one seed is also fine) and pick the best ones.\n2. I would like to see some results or explanation whether the topology-aware scoring system can work on objects with concentric patterns, e.g., cones, cylinders.\n3. Some explanation on whether the proposed M-DPO can be integrated to more compact mesh tokenization.", "limitations": "Yes.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The proposed topology-aware scoring system is well-designed and addresses the challenges of previous methods to produce meshes with good topology.\n2. The proposed M-DPO provides fine-grained supervision and is shown to be effective. The limitation of standard DPO and how it leads to the design of M-DPO are well-explained. \n3. Ablation study shows the effectiveness of the proposed methods.\n\nWeaknesses:\n1. The evaluation for this line of works, including this paper can be tricky. The qualitative comparison is usually cherry-picked. In addition, the quantitative evaluation is done on 100 artist-designed meshes and 100 AI-generated meshes selected by the authors. The selection of test set can introduce bias favorable for the proposed method. Moreover, different generation seeds can produces vastly different results. It is difficult to produce fair and unbiased evaluation.\n2. I wonder if the topology-scoring system and finetuning is compatible for shapes with triangles in a concentric circle (for example, Figure 7 right side in https://developer.nvidia.com/blog/high-fidelity-3d-mesh-generation-at-scale-with-meshtron/). It seems those circular patterns will get low quad ratio, angle quality, and aspect ratio.\n3. The quality-aware local masking used for M-DPO seems not trivial to be implemented for more efficient mesh tokenizations (e.g., MeshAnythingV2, BPT, EdgreRunner). I understand the choice of using naive tokenization is explained in Section 3.1 of the paper. However, it also means that the proposed method is limited to the least efficient tokenization that results in long generation sequence.", "was_revised": true, "final_justification": "My main concern about biased evaluation is well addressed. This paper is to my knowledge, the first to carefully address the topology aspect in artist-created mesh generation in a scalable manner. Although the proposed optimization is only well-suited for face-level tokenization, it can inspire future research direction. Thus, I recommend acceptance of this paper.", "rebuttal": "We appreciate your insightful feedback. We address your concerns below.\n\n**1. On Evaluation Bias and Cherry-Picking**\n\n**Response:** We appreciate your concern. A primary goal of our extensive pre-training was to enhance model stability and success rate, allowing us to consistently generate high-quality 10k-face meshes in a single pass without needing to cherry-pick from multiple runs. Our RL phase further refines topological quality to facilitate clean quad conversion. To demonstrate robust generalization, we evaluated our model on the complex examples from the first page of the TRELLIS website. While baseline models struggled to handle these cases, ours produced reasonable results, even for high-complexity meshes requiring up to 20k faces. Though our topology quality decreases at such high face counts, it remains significantly superior to other methods, as shown below.\n\n| Method | HD (↓) | TS (↑) | BER (↓) |\n| :--- | :--- | :--- | :--- |\n| MeshAnythingV2 | 0.582 | 64.7 | 0.0813 |\n| BPT | 0.571 | 65.5 | 0.0759 |\n| DeepMesh* | 0.503 | 68.2 | 0.0710 |\n| **Ours Pretrain** | **0.363** | **70.3** | **0.0297** |\n| **Ours M-DPO** | **0.284** | **72.5** | **0.0118** |\n\n**2. On Compatibility with Special Topologies**\n\n**Response:** This is an excellent technical point. Our quad-biased Topology Score (TS) is a deliberate design choice to align with production standards in game design, where quad-dominant meshes are heavily favored. Our M-DPO framework is robust to this, as it balances both global and local quality scores, preventing minor topological constraints from degrading the overall quality of a complex mesh. For instance, in the car wheel example (Figure 11), while the central hub is formed with only two triangles, the model correctly generates a clean, circular structure for the outer tire. We have also tested this on other assets like headphones and observed that circular structures are often composed of triangles arranged in a way that facilitates easy conversion into clean rectangular patches. This results in a topology that is highly acceptable for artists and requires only minimal edits to finalize.\n\n**3. On Integrating M-DPO with Compact Tokenizers**\n\n**Response:** This is a key question regarding the trade-off between efficiency and generative performance. Our choice of an uncompressed representation is deliberate, as the hierarchical nature of our Hourglass Transformer naturally preserves geometric semantics from the coordinate-level up to the face-level. In contrast, compact methods like BPT, while enabling faster convergence by fitting more faces into the context window, often disrupt this inherent geometric structure. This creates a clear trade-off: our approach requires more extensive pre-training but achieves superior final performance by maintaining high-fidelity representations. The table below quantifies this relationship.\n\n| Method | Representation | Pre-Training Cost | Final Performance (HD ↓) |\n| :--- | :--- | :--- | :--- |\n| DeepMesh* / BPT | Compressed | ~128 GPUs / 4 days | ~0.503 |\n| **Ours (Pre-trained)** | **Uncompressed** | **256 GPUs / 10 days** | **~0.363** |\n\nFor researchers wishing to apply our Mesh-RFT framework to compressed formats, we suggest elevating the masking unit from the *face-level* to the *patch-level*. However, this may dilute the fine-grained refinement, as a single patch could contain a mix of both high- and low-quality faces, likely making the optimization less precise than our face-level approach. Nevertheless, it represents a promising direction for future research, which we will detail in our final paper."}, {"review_id": "qrvwuNmyxO", "reviewer": "Reviewer_2AUC", "summary": "This paper presents Mesh-RFT, a novel framework for improving the quality of generated 3D meshes by fine-tuning pre-trained models. The core problem addressed is that existing methods often produce meshes with local artifacts (e.g., non-manifold geometry, holes, inconsistent density), and standard reinforcement learning (RL) approaches using global, object-level rewards are ill-suited to fix these fine-grained errors. The authors' main contributions are twofold: 1) They introduce an automated and objective system for creating a preference dataset. 2) They propose Masked Direct Preference Optimization (M-DPO), a novel extension of DPO. Experiments show that Mesh-RFT significantly outperforms both the pre-trained baseline and methods using standard global DPO. The results demonstrate substantial improvements in quantitative metrics like Hausdorff Distance and the proposed Topology Score, as well as in qualitative visual fidelity, leading to more coherent and \"production-ready\" 3D assets.", "questions": "1. On the Topology Score (TS) Hyperparameters: The weights for the four sub-metrics in the TS calculation (w1=0.4, w2=0.2, etc.) are crucial for defining what constitutes a \"good\" mesh. Could you please provide more justification for this specific choice of weights?  \n2. On the Generality of Quad-Based Metrics: The reward function is heavily biased towards good quadrangulation. Could you discuss the potential limitations of this approach for applications where high-quality triangular meshes are the desired end-product?", "limitations": "yes", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "### Strengths:\n- Novelty and Significance of the Method: The primary strength of this paper is the introduction of Masked DPO (M-DPO) for localized 3D mesh refinement. While DPO is an existing technique, its application with a spatially-aware mask to focus optimization on specific problematic regions of a 3D model is a clever and highly effective idea. It directly addresses a well-known limitation of global reward signals in complex generation tasks, providing a much-needed mechanism for fine-grained control. The problem of generating high-quality, topologically sound meshes is of great practical importance for computer graphics, gaming, and simulation, making this contribution highly significant.\n- Scalable and Objective Preference Data Generation: The proposed topology-aware scoring system (BER and TS) is a major advantage over prior work (e.g., DeepMesh) that relies on manual human preference labeling. By creating an automated pipeline for generating preference pairs, the method becomes more scalable, objective, and reproducible. This avoids the bottlenecks and biases associated with human annotation.\n- Strong Empirical Results and Thorough Evaluation: The paper presents a comprehensive set of experiments, including both quantitative and qualitative comparisons. The results are compelling, showing consistent and significant improvements over strong baselines across multiple metrics. The inclusion of a user study (US) further strengthens the claim that the generated meshes are perceptually superior.\n- Excellent Clarity and Presentation: The paper is exceptionally well-written and easy to follow. The motivation is clear, the method is explained logically with the aid of excellent diagrams and the results are presented effectively.\n\n### Weaknesses\n- Justification of the Topology Score (TS) Weights: The Topology Score is a central component of the preference generation pipeline. It is defined as a weighted sum of four sub-metrics. The paper states these weights are \"empirically set\" without further justification. The choice of these hyperparameters seems critical to the quality of the preference dataset, and a lack of analysis on their sensitivity or a more principled reason for their values makes this part of the method seem somewhat ad-hoc.\n- Potential Bias of the Quad-based Metrics: The TS metric is heavily reliant on an evaluation of a derived quadrilateral mesh. While quad meshes are indeed preferred in many industrial modeling pipelines (e.g., for subdivision surfaces), this is not a universal truth.  The strong preference for \"quad-ability\" might introduce a bias, potentially penalizing valid and high-quality triangular structures.\n- High Computational Cost: The pre-training requires a substantial amount of computational resources (256 GPUs for 10 days), which places it in the category of large-scale foundation models. While the fine-tuning is faster, the overall cost is a significant barrier to reproducibility and further research by smaller labs. This should be highlighted as a practical limitation.", "was_revised": true, "final_justification": "The authors' rebuttal has satisfactorily addressed my initial concerns. The new ablation study provides a solid empirical justification for the chosen Topology Score (TS) weights, and the authors clarified that the deliberate quad-bias is a reasonable design choice for their goal of producing \"production-ready\" assets. The core contribution, Masked DPO (M-DPO), is a novel and highly effective method for localized mesh refinement. The paper presents compelling quantitative and qualitative results that demonstrate significant improvements over existing baselines. This is a strong paper and I recommend acceptance.", "rebuttal": "We thank you for your strong support and insightful feedback. We address the weaknesses you raised below.\n\n**1. On the Justification of Topology Score (TS) Weights**\n\n**Response:** Our TS weights were optimized through iterative industrial benchmarks. To provide empirical support, our ablation study on 100 Hunyuan2.5 meshes shows that weight adjustments primarily influence local topological characteristics, not global geometry (HD remains stable, with Δ < 0.002).\n\n| Weight Config (w1/w2/w3/w4) | HD (↓) | BER (↓) | US (↑) | Topology Characteristics |\n| :--- | :--- | :--- | :--- | :--- |\n| Default (0.4/0.2/0.3/0.1) | 0.2411 | 0.0015 | 40% | Balanced squares/diamonds |\n| Equal Weights (0.25 each) | 0.2402 | 0.0018 | 37% | More triangles (merging failures) |\n| High w₂ (0.4/0.3/0.2/0.1) | 0.2420 | 0.0016 | 42% | Rectangles dominant |\n\nThis analysis reveals three key findings:\n1.  **Weight sensitivity is local and topological:** Adjustments alter face-type distribution without significantly affecting the global shape.\n2.  **Equal weights impair quad merging:** This configuration often results in non-coplanar triangle pairs, hindering quad conversion.\n3.  **Higher w₂ improves aesthetics and utility:** It boosts user preference by producing more rectangular quads, which streamline UV unwrapping workflows.\n\nThank you for highlighting this nuance. Based on these findings, we will adopt the **High w₂** configuration in our final model to optimize for both user preference and practical utility.\n\n**2. On the Generality of Quad-Based Metrics**\n\n**Response:** We agree our metric is quad-biased, and this is a deliberate design choice to align with industry standards in pipelines like gaming and animation, where quad-dominant topology is critical for stable deformations during rigging, efficient UV unwrapping, and artist-friendly edge-loop editing. Our focus on generating \"production-ready\" assets therefore necessitates this optimization for high-quality quad topology.\n\n**3. On Computational Cost**\n\n**Response:** We agree the pre-training cost is substantial, and we made this investment to critically improve model stability and reduce generation failures. Our experiments show a direct correlation between extended pre-training and a lower \"breakage rate\"—the percentage of meshes with critical artifacts.\n\n| Model | Breakage Rate (↓)* |\n| :--- | :--- |\n| DeepMesh | ~85% |\n| Ours (3-day pre-train) | ~80% |\n| Ours (10-day pre-train) | ~5% |\n| **Ours (M-DPO)** | **<4%** |\n\n*\\*Breakage Rate defined as the percentage of meshes with non-manifold edges and holes on the test set.*\n\nCrucially, the M-DPO fine-tuning—which contains the core contributions of our paper—adds only 8 hours of GPU time, making the refinement stage highly accessible. We will release the robust 10-day pre-trained model to lower the barrier to entry for the research community."}]

Author rebuttal:

Meta-review: The paper received unanimous positive evaluations, with two strong accepts and two accepts. Reviewers found Mesh-RFT to be well-written, novel, and practically significant, particularly for its introduction of Masked Direct Preference Optimization (M-DPO) and an automated scoring system for mesh topology refinement. They highlighted its quantitative and qualitative improvements, the scalability of preference data generation, and clear perceptual gains demonstrated through user studies.

Some concerns were raised regarding fairness in comparisons with DeepMesh (reviewer #cRc7), the limited scope of qualitative ablations for scoring metrics, the justification of Topology Score (TS) weights (reviewer #dibn), and the possibility of quad-bias in the evaluation (reviewer #ynVT). The authors effectively addressed these issues by providing new ablations—including H-DPO baselines and TS weight studies—clarifying the rationale for quad-biased metrics, and supplying additional evidence of stability and convergence.

Following these clarifications, reviewers agreed that their main concerns had been resolved, raised their ratings, and emphasized that Mesh-RFT represents the first scalable approach to mesh topology refinement with both strong theoretical grounding and practical value. Given the unanimous positive consensus, the AC recommend acceptance.
Decision: Accept (spotlight)