Title: Auto-Connect: Connectivity-Preserving RigFormer with  Direct Preference Optimization
Venue: NeurIPS 2025
Accepted: yes
Keywords: Rigging; Skinning; Post-training;
Abstract: We introduce Auto-Connect, a novel approach for automatic rigging that explicitly preserves skeletal connectivity through a connectivity-preserving tokenization scheme. Unlike previous methods that predict bone positions represented as two joints or first predict points before determining connectivity, our method employs special tokens to define endpoints for each joint's children and for each hierarchical layer, effectively automating connectivity relationships. This approach significantly enhances topological accuracy by integrating connectivity information directly into the prediction framework.
To further guarantee high-quality topology, we implement a topology-aware reward function that quantifies topological correctness, which is then utilized in a post-training phase through reward-guided Direct Preference Optimization. 
Additionally, we incorporate implicit geodesic features for latent top-$k$ bone selection, which substantially improves skinning quality. By leveraging geodesic distance information within the model's latent space, our approach intelligently determines the most influential bones for each vertex, effectively mitigating common skinning artifacts.
This combination of connectivity-preserving tokenization, reward-guided fine-tuning, and geodesic-aware bone selection enables our model to consistently generate more anatomically plausible skeletal structures with superior deformation properties.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper aims to solve the problem of 3D mesh rigging. This method primarily consists of 3 key components: 1) a hierarchical skeletal representation, 2) DPO-based post-training to ensure better rigging refinement, and 3) a novel skinning weight prediction module. The paper conducts extensive experiments, achieving superior results over existing methods both qualitatively and quantitatively, demonstrating the effectiveness of this approach.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This work introduces DPO-based post-training for refinement, which is intuitive and effective.\n2. The results of this paper are excellent, outperforming existing works both qualitatively and quantitatively.\n3. The paper is well-written, easy to understand and follow.\n\nWeaknesses:\n1. In Table 1, the results for Magic-spatial and Magic-hier are significantly worse than the results reported in their original papers. Specifically, Magic-spatial yields better results than the proposed method. Please confirm it.\n2. The ablation studies seem insufficient. For example, comparisons with baseline tokenization methods (e.g., UniRig's), and validation on the data augmentation. The authors should provide complete quantitative comparisons for these ablations. \n3. Table 4 indicates that DPO provides only limited quantitative gains. Does pre-training alone achieve results far superior to the current SOTA? The main improvement of the token is the addition of  \\< E2 \\>  compared to other BFS skeletons. The rigging results of hands in Fig. 4 and Fig. 9 seem contradictory.\n4. Performance on in-the-wild data is not good. For instance, in the first row of Table 6 (left), there should not be bones on the guns. In the second row, the hand bones are incorrect. I intuitively feel that the proposed DPO encourages skeleton expansion but overlooks semantic information.\n\nQUESTIONS:\nOverall, I recommend that the authors:\n1. Explain why the results in this paper are inconsistent with the original MagicArticulate paper.\n2. Provide a more thorough explanation of the similarities and differences between their token representation and existing works, along with an analysis of its merits and limitations.\n3. Supplement additional ablation results to thoroughly validate the effectiveness of the proposed modules.\n\nAs for the DPO-based post-training, the current framework, after sampling shapes, may not fully capture the semantic information of the mesh, specifically, whether certain parts should be rigged with bones. I wonder if incorporating richer semantic information could enhance the quality of skeleton prediction.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel method for automatic rigging of 3D meshes. The authors claim three main contributions: (a) a connectivity-preserving tokenization scheme that converts a skeleton tree into a token sequence by performing BFS traversal and appending special tokens; (b) a post-training phase that aligns model outputs with topology-aware rewards, complementing the next-token prediction strategy used in pretraining; and (c) a geodesic distance-based MLP module for bone prediction in skinning weight estimation.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The paper is clear and easy to follow.\n2. The proposed techniques are well-motivated and technically sound.\n3. The method demonstrates superior performance over baselines on two datasets.\n4. The paper shows that the proposed skinning strategy can be coupled with various existing methods and provides performance gains.\n5. The method performs well on in-the-wild meshes (e.g., those generated by AI models).\n\n---\n\n**Weaknesses**\n\n1. The current ablation of the tokenization strategy is weak, despite it being one of the main contributions. It only compares the average token length. It is necessary to explicitly describe the tokenization strategies used in baseline methods and compare model performance using different strategies both qualitatively and quantitatively.\n\n2. Figure 7 does not sufficiently demonstrate the benefits of data augmentation, as it only showcases results with data augmentation and lacks results without it. Including quantitative ablation studies on data augmentation would strengthen the paper.\n\n3. To make the paper self-contained, a brief introduction to how DPO works should be included. For example, explain how the reference policy is selected and the relationship between the reference policy and the optimized policy.\n\n4. An explanation of how skinning weights are visualized in Figure 5 would be helpful for readers.\n\n5. In the method section, many abbreviations (e.g., DPO, SFT, CD-J2J, CD-J2B, CD-B2B) should be defined upon their first appearance.\n\nQUESTIONS:\n1. **What is the relationship between Bone Probability Prediction and Skinning Weight Prediction?** Are you first predicting a set of bones for each vertex, and then predicting the skinning weights for these vertex-bone pairs? Or are you directly predicting skinning weights over all bone-vertex pairs? Additionally, what is the relationship between the Binary Cross-Entropy loss (line 229) and the Kullback-Leibler Divergence loss (line 236)? Are they computed over the same set of variables (i.e., the skinning weights $w_{ij}$)?\n\n2. **Line 192:** Given a set of predicted joints and a set of ground-truth joints, how do you determine which joints are considered *shared joints*?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a three-stage pipeline for rigging a character mesh. The first one is bone prediction in the form of tokens, which is a first for this domain. Second, improving bone hierarchy with a second reinforcement learning step. Third, skinning weight prediction that improves on existing top-k methods. Together these steps significantly outperform prior work and also individually each contributions improves on respective baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Well-justified use of Direct Preference Optimization (DPO) for the problem of rig generation\n- clear methodological difference to prior method that have weaker constraints on skeleton connectivity\n- the probabilistic skinning weight prediction (k most probable instead of k nearest) is a nice addition that rounds up the publication\n- Eventhough some of the improvements shown in the ablation study appear small numerically, the visual results demonstrate clear improvements.\n- Overall consistent and significant improvements on the SOTA\n\nRecommendation:\n- the supplemental material is extensive, yet it would be good to have a summary of limitations in the main paper\n\nWeaknesses:\n- while each of the model components makes sense, the justification lacks a discussion on why it is not possible to do step 1 and 2 at once. Would it not possible to add (some of) the objectives used for DPO already at training time? Could you justify the sequential steps?\n- are snake like shapes the only limitation? Looking at the last row (ant) in Figure 11 of the Art-XL2.0 data, it appears that the last character segment is not rigged, or can it rotate with the last endeffector (depends on how the skeleton hierarchy ist defined). Any other issues?\n\nMinor:\n\"160 Shape-conditioned generation. we randomly samp\" -> We\n\nQUESTIONS:\n- Line 263 \"Since RigAnything [40] does not share its code, we cannot compare to it\" - would it be possible to compare on data they were evaluating on? At least qualitatively?\n\nLIMITATIONS:\nSee above, limitations could be evaluated better. E.g. with a selection of the 10-20 worst test cases, perhaps in relation to other method's worst cases.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies rigging prediction from meshes. The input is a triangle mesh; the output is a skeleton and the corresponding skinning weights. The pipeline consists of three phases. First, the input is tokenized from sampled point clouds. The output is parameterized in a tree order and can be generated by a transformer. After this pretraining stage, post-training is conducted using both SFT and DPO, where the reward includes some topology-aware distances. Finally, the skinning weights are predicted by a small MLP that accepts both the skeleton and mesh vertices and performs a soft assignment. Convincing results and real data examples are provided in the paper and the supplemental document. The supplemental document further includes failure cases and more side-by-side comparisons.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n- The parameterization is reasonable, and the overall architecture design is effective and simple.\n- The result quality appears convincing and surpasses prior art.\n- Sufficient experimental evidence supports the paper’s contributions.\n\n---\n\n**Weaknesses:**\n\n- I do not see major weaknesses in this paper.\n- More intuition about the non-differentiable DPO stage would help readers understand why such reward metrics were chosen. From the qualitative ablation study, I can tell that the DPO stage has a noticeable effect, but there is a lack of further explanation.\n- The use of the word \"geodesic\" in the skinning section is misleading. It would be better replaced with \"geometric,\" since \"geodesic\" has a specific mathematical meaning, and there do not appear to be any explicit geodesic distances on the manifold in the current design.\n\nQUESTIONS:\n- Could you elaborate on the last two points in the weaknesses section?\n- Regarding the rigging problem, what is the current frontier in this area? How does the model perform on totally out-of-distribution cases? How does it perform on strictly multi-body, piecewise-rigid objects?\n\nLIMITATIONS:\nSufficiently discussed", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}