Title: Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Autoregressive Mesh Generation

Reinforcement Learning

Topology Optimization
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}