Auto-Connect: Connectivity-Preserving RigFormer with  Direct Preference Optimization
Authors: Guojingfeng
Jian Liu
Jinnan Chen
Shiwei Mao
Changrong Hu
Puhua Jiang
Junlin Yu
Jing Xu
Qi Liu
LiXin Xu
Zhuo Chen
Chunchao Guo
Venue: NeurIPS 2025 poster ()
Keywords: Rigging; Skinning; Post-training;
Primary area: applications
TL;DR: We present Auto-Connect,  a method that establishe a new standard for automatic character rigging and skinning, offering a more reliable and effective solution for animation pipelines

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.

Auto-Connect: Connecivity-Presevvinn RigFormer with Direct Preference Optimization
Jingfeng Guo Jian Liu Jinnan Chen Shiwei Mao Changrong Hu Puhua Jiang 4.7 Junlin Yus,7 Jing Xu², Qi Liu't, Lixin Xu², Zhuo Chen?, Chunchao Guo?
South China University of Technology
Hong Kong University Science and Technology NNational University of Singapore
Tsinghua Shenzhen International Graduate School University of Science and Technology of China
Beijing Normal University TTencent Hunyuan
https::/aattocoonectrrt github.
Abstract
We introduce Auto-Connect, novel approach for automatic rigging that explic- itly preserves skeletal connectivity through connectivity-preservee tokenization scheme. Unlike previous methods that predict bone positions represented as two joints first predict points before determining connectivity, method employs special tokens to define endpoints for each joint's children and for each hierar- chical 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 topol- ogy, we implement topology-awan reward function that quantifies topological correctness, which is then utilized in post-training phase through reward-guid -wwrdddguiedd 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 comnectivity prreeerrii tokenization, reward- reward-guided fine-tuning, and geodesic- aware bone selection enables our model to consistently generate more anatomically plausible skeletal structures with superior deformation properties.
1 Introduction
The creation of highly detailed 3D shapes [1, 2, 2, 6, 9, 10, 11] and digital avatars [12, 13, 14, 15, 16, 17, 18, 3, 19, 20, 21, 22] has become increasingly accessible through advancements in generative modeling technologies. Despite these impressive capabilities static content generation, these models remain fundamentally limited for dynamic applications. Some early works address this limitation by predicting dynamics through per-vertex deformation or physical simulation [23, 24, 25, 26, 27, 28, 29, 30, 31]. In contrast, rigging offers parametric representation that establishes skeleton for 3D model and defines surface deformation response skeletal movement which is compatible to graphics pipeline. Despite its critical importance in animation pipelines, auto-rigging
"Equal contributions. Work primarily done during internship at Tencent Hunyuan Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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remains challenging problem due to the complexity of accurately modeling skeletal structures, ensuring proper joint and bone connectivity, and preventing artifacts during animation.
Early approaches to auto-rigging relied on fixed topology templates [32, 25], which limited their applicability across diverse character morphologies. Later developments, such RigNet [33], employed more flexible strategies including clustering for joint position acquisition and Minimum Spanning Tree (MST) algorithms for topology construction. Additionally, various optimization-bant methods [34, 35, 36, 37] have been proposed to generate character-specific rigs, but often require additional optimization cost and suffer from generalization issues. Recent advancements in generative models and the expansion of 3D datasets have made auto-rigging more scalable.
Current learning- baanni approaches typically fall into two categories: methods [38, 39] that predict bone positions without explicitly considering the connectivity between joints, and methods [40] that first predict skeletal joint points before determining their connectivity. As result, these approaches frequently produce suboptimal topology quality, primarily because their representations fail to effectively capture the inherent topological relationships within skeletal structures. This deficiency frequently leads to unrealistic deformations in animations.
To address these limitations, we introduce Auto-Connect, novel approach that explicitly preserves skeletal connectivity. By incorporating a connectivity prreeevriiiieeiiigggggg tokenization scheme, Auto-Connect ensures that joints and bones are connected in way that maintains the inherent structure of the skeleton, overcoming the topological errors common in previous methods. Our method defines endpoints for each joint's children and for each hierarchical layer, effectively automating connectivity relationships within the prediction framework itself. This fundamental redesign significantly enhances topological accuracy by integrating connectivity information directly into the model's representation. Based on this representation, we design an autoregressive training framework for skeleton trees, RigFormer, which incorporates level position embedding and a set of data augmentation strategies.
Furthermore, purely next-token prediction with cross-entropy loss focuses solely on local conditional distribution modeling, failing adequately capture joint distribution properties. To address this limitation, we introduce a joint distribution constraint during post-training through a DPO loss framework. Our approach primarily emphasizes topology improvement by incorporating carefully designed topology-awan aware reward functions. We evaluate rig quality based on joint position accuracy and topological quality. For position accuracy, we calculate the chamfer distance between predicted joint positions and ground truth, providing a robust measure of geometric fidelity. For topological quality, we employ two complementary metries: Tree Edit Distance, which measures the cost of transforming the predicted skeleton topology into the ground truth through a series of edit operations, and Hierarchical Jaccard Similarity, which quantifies the overlap between hierarchical structures while considering parent-child relationships. This reward function is then utilized in post-training phase through our reward-guize guided Direct Preference Optimization (DPO), guiding the model toward generating both geometrically accurate and topologically sound skeletal structures. Finally, we incorporate implicit geodesic features for latent top-k bone selection, which substantially improves skinning quality by leveraging spatial relationships within the character's geometry. This approach implicitly determines the most influential bones for each vertex, effectively mitigating common skinning artifacts, particularly stretching phenomena that occur during extreme deformations.
Through extensive experiments on public benchmarks, we demonstrate that Auto-Connect substan- tially outperforms previous state-of-the-art methods, achieving superior results in joint location accuracy, topological consistency, and skinning quality. We summarize our contributions as follows:
We introduce Auto-Connect, a novel automatic rigging pipeline that explicitly preserves skeletal connectivity through a connectivity- preserrvig tokenization scheme coupled with an enhanced pre-training framework, RigFormer.
We develop a topology-ave this, present a rigging post- post-training phase through our swardd-gudde DPO to further improve topology quality. To the best of our knowledge, this is the first work to combine reinforcement learning with the rigging task.
reward function tailored for skeleton tree structures, and build upon
We present a alug-and-play geodesic-aware bone probability prediction module that incorporates implicit geodesic features to dynamically determine the top-k bone for each vertex, effectively mitigating common skinning artifacts.
2

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fre-fraring
RigFormer
Topology oxare Reward Criter on
Reference Risformar
Policy RigFarmer
Shoze Oscodar
1005>
tapple
Cuo ALas
Cemeatt wity proder rigg Detelenize
Reward quieed DP3
Gensrnted Soakeee
Preference Parrs
Stage2: Weight Prodiction
Trin
Goodesio owane Bone Probubiitty Nocile
Glasning Weight Predictiar
Prezer
< [[ddd
Figure I: Overview of the Auto-Connect. The pipeline consists of three main stages. In the Rigging Pre- training stage, point cloud sampled from the input 3D mesh is processed by the shape encoder extract geometric features, which are subsequently into our autoregressive RigFormer to generate token sequence. The generated sequence is then processed using our connectivity -ttttyerreiinng detokenization to construct the skeleton tree. In the Rigging Post-training stage, preference pairs are constructed using our topology-ave -ware reward criterion, and RigFormer fine-tuned with reward-guided DPO for preference-drive optimization. Finally, in the Skinning Weight Prediction stage, the generated skeleton and mesh vertices serve imput. Our geodesic-ave aware bone probability prediction module is employed to implicitly determine the most influential bones to predict the skinning weights, enabling mesh animation.
Related Work
2.1 Automatic Rigging and Skinning
Automatic Rigging can be categorized into template-based [32, 42, 43, 44] methods and template- free [33, 45, 40, 38, 39] paradigms. Template-based methods mainly focus humanoid characters and are limited to fixed skeleton topologies, restricting their use for diverse character types. Template- free methods like RigNet [33] and VninSkelVolNet [45] use regression and clustering for joint prediction, along with MST for connectivity. However, their hybrid architectures face optimiza- tion challenges due non-differentiable clustering and MST operations. RigAnything [40] and MagicArticulate [38] use autoregressive rigging but neglect the skeleton's hierarchical structure and parent-child joint connections. This forces them either rely on additional modules for joint connectivity or re-encode parent joints multiple times, which increases sequence length and computa- tional cost. UniRig [39] attempts address these shortcomings by extracting bone chains through Depth-First Search. However, the connections between bone chains rely on heuristic connection rules, which merge joints within a predefined distance threshold, making it non-end-to-end and prone to compounding errors. Additionally, it often predicts the termination token prematurely. leading to incomplete skeletal structures with missing bone chains. In contrast, our method overcomes these limitations by integrating topology information directly into the model's representation. Current skinning methods [33, 46, 47] statically select the k-nearest bones for each vertex and assume that only these bones influence the vertex, then Graph Neural Networks predict skinning weights. However, complex meeshskeeeeton topologies often render distance calculations unreliable, leading to critical binding errors. To address this, present plug-au alug-and-ppl and-play geodesic- module that dynamically identify the most probable influencing bones conditioned on geodesic feature cues.
2.2 Autoregressive Models for 3D Generation
Autoregressive transformers [48, 49] have radically transformed visual generation [50, 51, 52, 53, 54, 55] through their sophisticated sequential approach of synthesizing images using discrete tokens derived from image tokenizers. This paradigm has achieved remarkable success by decomposing the complex image generation task into a series of manageable token prediction steps, enabling more coherent and controllable outputs. Building upon this foundation, recent work [4, 56, 57, 58, 59, 60, 61] has introduced specialized mesh tokenizers that extend the autoregressive framework to 3D mesh

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generation. These methods effectively discretize 3D geometry into sequential tokens that can be predicted in an autoregressive manner, similar language modeling. Our method builds upon these advances, introducing a novel connectivity-presenee tokenizer that enables more accurate, diverse, and artist-intuitive skeleton generations.
2.3 RLHF with Direct Preference Optimization
With the rapid advancement of Large Language Models (LLMs) [62, 63] and Vision- Languuee Models (VLMs) [64, 65, 66], aligning policy models with human preferences has become increasingly critical. Post-training techniques such as Reinforcement Learning with Human Feedback (RLHF) and Direct Preference Optimization (DPO) aim to improve model performance by reflecting user intentions. Early RL.F methods [67] trained on manually labeled preference pairs and optimized
policies using Proximal Policy Optimization (PPO) [68], but faced instability and high computational costs. DPO addresses this by removing the need for explicit reward model and using an implicit reward function based on PPO optimality, optimizing policies via maximum likelihood estimation with the Bradley-Terry model [69]. Building upon this foundation, we design a topology-awan reward function for skeleton trees and propose a reward- guided DPO to encourage the generation of topologically accurate skeletal structures. To the best of our knowledge, this is the first successful implementation of DPO in rigging tasks.
Method
Our Auto-Connect comprises three core innovations, as illustrated in Figure First, Section 3.1 introduces the rigging pre-training stage with a novel cuanectivity-presevvinn tokenization scheme and RigFormer training framework. Building upon this foundation, Section 3.2 presents the rigging post-training stage using our reward-guider DPO with the proposed topology-aware reward criteria. Finally, Section 3.3 details the proposed geodesic-aware bone probability prediction module that enables plug-and-pe aad--ndd-laa -nay integration with existing skinning methods for precise deformation control.
3.1 Rigging Pre-training
Unlike cemplate-based methods [41, 42, 43, 44] that rely on fixed topologies for generating specific skeleton categories, or prior template-fis frre methods [33, 40, 38, 39] that overlook skeleton topology encoding, we propose
tokenizer that efficiently encodes both skeleton hierarchical structure and parent-child joint connections. This tokenizer enables the automation of joint connectivity in an autoregressive paradigm, allowing for the generation of diverse skeletons.
Tokenization. Given an input mesh and skeleton, we first nor- malize them into a unit cube space |-0.5, 0.5) and apply n-bit quantization to the joint coordinates via
connectivityppreservvi
lovel
Height onder
lewel
leve
de = L(ik + +.5) 2" I {x (1)
Trput dhelleton
Skeleton tree
where ja and de denote the original and dis- cretized coordinates, respectively. Next, we tra- verse the skeleton tree in breadth-first (BFS) order, as shown in Figure 2. Specifically, based on the standard BFS traversal, we insert spe- cial token <E1> after visiting all the child joints of parent joint indicate the endpoints for the current parent's 's children. Note that leaf joints not at the last level still need an <E1> to signify this. Similarly, after traversing all the joints in the current level, we insert another special token <E2> to mark the completion of that level. Additionally, we incorporate a height- aware spatial prior by sorting child joints under each parent joint according to their z-axis coordinates, which reduces the difficulty for the model in regressing joint positions. Finally, we obtain 3J + M + L. tokens, where J the number of joints, the number of levels in the skeleton tree, and M the total number of joints in the first L levels.
4 [.
kwel
lewel
Iwee
Figure 2: Connectivity- preseeving tokenization pro-
cess. The number indicates the joint indices.

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good
bed
good
bod
good
bad
Roward=4.44
Reward=1.59
Reward=2.73
Figure 3: Examples of the collected preference pairs. Skeleton with higher reward exhibit superior topology better align with human intuition, making them the preferred choice.
Shape-conditioned generation. we randomly sample N = 20000 surface points from the input mesh construct a point cloud representation P E TRNN x3 with corresponding surface normals N E R Nx3, These geometric primitives are encoded through pre-trained Michelangelo [70] encoder g.. which captures both local geometric details and global shape semantics. The generates shape condition Cg = Eg (P,N), serving as the context for the autoregressive generation process.
RigFormer. We adopt standard transformer with parameter 0 model the skeleton sequence and leverage cross-attention for shape conditioning. The training process is achieved using the next token prediction loss:
T II (T. Tia- @ Chu-1 Cgi 0)
Sagee
(2)
where T' denotes the total sequence length. To explicitly model skeletal hierarchy, we additionally inject level position embedding e, into the transformer. Here, (T: Tia-1 + 1 denotes the conditional probability of token т; given the preceding tokens and level embeddings in the sequence.
During inference, the generation process starts with only the shape tokens input and progressively generates skeleton tokens. The resulting token sequence is then converted into the final skeleton using our connectivity connectivity-preseviing detokenization. With the proposed special tokens <E1> > and <E2>, the hierarchical structure and parent-child joint connections can be automatically determined.
3.2 Rigging Post-training
Next-token prediction method focuses only local conditional distributions, neglecting the critical aspects of joint distribution modeling, especially for topology preserving. We implement a joint distribution constraint during the post-training phase, utilizing DPO loss to further improve the topology quality. Specifically, we introduce a topology-awar reward function to evaluate the quality of skeletons. Based on this criterion, construct preference pairs and propose reward- guidedd DPO to fine-tune our RigFormer in preference- seceereence-driven manner. Moreover, to prevent overfitting, we add the SFT auxiliary constraint loss during DPO training.
Topology-aware Reward. Given the predicted skeleton S, and ground truth skeleton S,, we evaluate the quality of the predicted skeleton in terms of spatial accuracy and topological fidelity. Spatial accuracy is measured by the Chamfer Distance (CD) between the generated and ground truth skeletons, which is calculated as the sum of CD-J2J, CD-J2B, and CD-B2B. Each reward decays linearly from to points as the CD value increases from to 10%, which can formalized as:
CD; max (1
RCD
Σ
0 E [0,3]
10%
JE(J2IJ2B.BB2
For topological fidelity, we first use the Tree Edit Distance (TED) to measure the minimum edit operations required transform the predicted skeleton into the ground truth skeleton. This value is normalized by the total number of joints in both trees to eliminate scale bias:
TED (Sp. Sg)
RTED
RTED {0,1]
1J + +IT
5

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GT
Ours
Magic-sptial
Magic hiii
UniRig
RigNet
Figure Qualitative comparison rigging result on Art-XL2.0 (top) and MR (bottom). Our connectivity- preserving representation effectively captures intrinsic skeletal topology, and reward-guided fine-tuning enables the generated skeletons to better align with artistic aesthetics. Additional results are provided in the appendix B.
where 1Tpl and [Ty] represent the number of joints. Then, we calculate the Hierarchical Jaccard Similarity (HJS), which evaluates the local topological accuracy of the shared joints Trommon'
(C()) (e (p(s) UC
RHJS
E [0,
Jecommon
JEJoammon
where Cpt) and Cg()) represent the children set of joint j in the predicted and ground truth skeletons respectively. The composite reward aggregates these 5-point scale:
Reward
RED
RTED PaIS
[0,
(6)
Spatial
Topological (2pts)
(3pts)
Preference Pair Construction. We generate four candidate skeletons for each input and select preference pairs using our topology-aware reward criterion. Specifically, pairs in which both skeletons reward below predefined threshold (set are discarded. When skeleton outperforms its counterpart by more than 0.5 points, the superior skeleton is chosen as the preferred one. Figure shows some selection cases of collected preference pairs.
Reward-guided DPO. Unlike standard DPO, which leverages only the binary preference order between two responses, our approach incorporates reward differences to exploit richer comparative information. By multiplying the DPO loss with the reward difference, we amplify gradients for pairs with larger disparities, guiding the model to better distinguish between high-fidelity and low-fidelity skeletons and enhance the discriminative gap during optimization. By training triplets of inputs x, high-reward outputs Ug. and low-reward outputs yb. the model learns to prioritize generating high-reward samples:
(oro( Vg: y6) = E ER. log o (Blog <(ol=)) Tarly,
3 Bog mec(vsla))
Uy) - (I, vo))
where B is a hyperparameter balancing the distance to the reference policy Tee set to 0.3 in our experiments, я denotes the policy model being optimized, r (x, My) and rx(x, Wh) the rewards assigned by our topology-awan better quality, they may still fall short of the perfect ground truth Ygt. which obtains the maximum 5 5-point reward. To address this limitation and ensure the model not only discriminates between good and bad cases but also retains foundational generative capabilities, we introduce auxiliary SFT loss, where the ground truth Vet is used to compute the next-token prediction loss. This loss LSFT mitigates excessive deviation from the pre- pre-trained knowledge base, ensuring stability during preference alignment. Finally, the total loss for the post-training stage is:
reward function. Furthermore, while high-reward skeletons exhibit
Cauge Loro (z, Fg. Bb) + XL (x., Vea)
(8)

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Table I: Quantitative comparison rigging result. denotes models trained Art-XL2.0 and tested
MR. MagicArticulate and UniRig cannot be trained on MR dataset as their training script not provided. indicate the best, second best, and third best performance respectively.
Method
Dataset
CD-J2J
CD-12B4
CD-B2B,
1oU1
Prec.
Rec.1
RigNet
7.587%
6.347%
6.366%
21.055%
21.015%
33.135%
Mugic-hier
3,435%
2.757%
2.393%
76.364%
78.121%
77.567%
UniRig
Ar-XXL.00
3.232%
2.540%
2.124%
75.571%
77.334%
77.323%
Magic-spatial
3.041%
2.479%
2.099%
78.675%
80.026%
80.085%
Ours
2.572%
2.030%
1.68%%
82.806
85,254 %
82.918%
RigNet Magic-hier"
6.375%
5.115%
5.245%
31.034%
24.034%
50.002%
4.119%
3.155%
2.780%
63.382%
57.889%
73.680%
Magic-spatial"
UniRig"
MR
3.797%
3.920%%
2.888%
3.138%
2.712%
2.437%
62.832%
63.831%
57.735%
56.230%
76.419%
15.667%
Ours
3.735%
2.816%
2.362%
66.488%
62.655%
75.059%
Ours
3.203%
2.436%
2.046%
73.108%
73.965% %
76.795%
3.3 Skinning Weight Prediction
The fundamental limitation of existing skinning methods [33, 46, 71] lies in their static bone selection paradigm-ther precompute vertex-bore distances to permanently select the k-nearest bones, and assume that the vertex is influenced only by these bones. In contrast, present plug-and-pla and-play geodesic-aware bone probability prediction module that dynamically identifies the most probable influencing bones conditioned implicit geodesic features.
Geodesic-aware Bone Probability Prediction Module. The inputs include the vertex positions, the vertex normal, the coordinates of joints, and the vertex-bone geometric distances computed from both raw and laplacian-sman smoothed meshes. These attributes are processed through a three-layer MLP to predict the influence probabilities of each bone for every vertex, and the k highest- probbbbity bones are selected, To optimize bone selection, we reframe the issue of whether the bone by impacts the vertex v; binary classification problem. Here, label of signifies influence, while label of 0 indicates no influence. Thus, this module minimizes the discrepancy between the chosen bone and
the actual bone using Seery-Croos--ntropp loss: Ls D (-6 --goog (b) (1-b)
8) log (1 - b)).
where à represents the ground truth labels, b denotes the predicted probabilities, and N is the number of vertices. For more details, please refer the appendix A.
Skinning Weight Prediction Module. To highlight the plug-and-plas ald-play nature of our bone probability prediction module, we integrate it with existing skinning methods. We consider the skinning weight matrix the probability of each vertex binding each bone. Thus, this module minimizes the discrepancy between the predicted skinning weights distribution and the actual distribution using
way where thij is the ground truth, wi is the predicted skinning weights, and B is the number of bones. Finally, the total loss for the skinning weight prediction stage Lstuge3 Ls + Lp.
Kullback- divergence loss: LD -EA Σラ Wij
(log Miss
Experiments
4.1 Implementation Details
Dataset. We evaluated our model on Articulation-XL.3 (Art-XL2.0) [38] and ModelsResource (MR) [33]. Art-XL2.0 provides 46k samples for training and 2k samples for testing, while MR dataset contains 2. lk training samples and 540 testing samples.
Data augmentation.
To enhance model robustness and generalization, we applied a comprehensive data augmentation strategy during the rigging pre-training stage. This includes the following com- ponents: 1) random mesh translations within [-0.3, 0.3]. 2) random axial rotations, 3) non-uniform scaling along each axis introduce variations in proportions, and 4) bone perturbation, where randomly selected bone is rotated by an angle sampled from gaussian distribution N (0, 25%).
7

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HeterSkinNat"
Sktere
Input skeleton
GT skinning weights
Skirming weights
Skinring weights
Li-norm errors
Figure 5: Qualitative comparison skinning result on Art-XL2.0 (top) and MR (bottom). Models marked with were trained using our geodesic-aware bone probability prediction module, which effectively mitigates the Ll-norm emorr and enhances skinning performance. Additional results are provided in the appendix B.2.
Table Quantitative comparison skinning result. Models marked with trained using geodesic- aware bone probability prediction module. Results
dataset are provided the appendix B.2.
Method
Dataset
Prec.1
Rec.1
avg L.
avg Dist.
RigNet
87.47%
56.04%
0.52
0.0090
RigNet*
87.67 %
59.98%
0.44
0.0079
NeuroSkinning
87.05%
55.51%
0.52
0.0089
Art-XL2.0
NeuroSkinning"
88.00%
61.31% %
0.45
0.0080
HeterSkinNet HeterSkinNet"
88.16%
60.18%
0.43
0,0079
89.38% %
61.13%
0.42
0.0075
Training details. The rigging pre-training stage uses batch size of 192, lasting 2 days on the Art-XL2.0 dataset and 10 hours the MR dataset, while the post-training stage runs epochs on 14k curated preference pairs. For the skinning weight prediction stage, we set k = 6 follow the bascline. Training uses batch size of 80, lasting day for the Art-XL.2.0 dataset and 10 hours for the MR dataset. See the appendix A.2 for more details.
4.2 Metrics and Baselines
Metries.
Consistent with RigNet [33], we evaluate rigging results using CD-J2J (Chamfer Distance between Joints), CD-J2B (Chamfer Distance between Joints and Bones), CD-B2B (Chamfer Distance between Bones), IoU (Intersection over Union), Precision, and Recall. For skinning results, we adopt Precision, Recall, Ll-norm error, and distance error to comprehensively assess bone identification accuracy, skinning weight precision, and deformation quality.
Baselines. For rigging results, compare approach against state-of-the-art approaches, in- cluding RigNet [33], MagicArticulate [38], and UniRig [39]. MagicArticulate is evaluated using both its proposed hierarchical (Magic-hier) and spatial (Magic-spatial) sequence orders. Since RigAnything [40] does not share its code, we cannot compare to it. For skinning results, we in- tegrate our geodesic-aware bone probability prediction module with three top-k-based skinning methods-RRiNee [33], NeuroSkinning [71], and HeterSkinNet [46]-to demonstrate its compatibil- ity and effectiveness. All vertex-to- toreex--t--ooeeeeee geodesic distance computations adhere the HollowDis proposed in the HeterSkinNet, with GPU-accelerated 256-resolution voxelization [72].
4.3 Comparison
Rigging Comparison. As quantitatively shown in our method achieves comprehensive im- provements across all metrics on both datasets. For skeleton location accuracy, we outperform Magic-spatial by +4.2% IoU, +5.2% Precision, and +2.9% Recall on Art-XLL2.0, while reducing 15% by CD-J2J, 18% by CD-J2B, and 20% by CD-B2B. The improvements over RigNet are even more pronounced. In terms of topological integrity, as qualitatively shown Figure 4, MagicArticulate's representation fails to capture the inherent topological relationships within skeletal structures, often

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Input image
Ours
Input image
Ours
GT
Full model
w/o DPO
Figure 6: (Left) Rigging results on mesh from in-the-wild images. We use off-the-shelf image-to-3l model Hunyuan3D 2.5 [3] to generated meshes from input images. (Right) Ablation study on DPO. The proposed reward-guided DPO learns human preferences to produce skeletons that better align with artistic aesthetics.
Table 4: Ablation study on DPO. Results
dataset are provided the appendix B.3.
Method
Dataset
CD-J2JL
CD-J2B
CD-B2B
IoUf
Prec.t
Rec.
DPO
2.695%
2.109%
1.750%
82318
84.234%
82.734%
An-XL2.00
2.572%
2.030%
1.683%
82.806%
85.254%
82.918%
resulting in unconnected joints and discontinuous skeletons (highlighted orange boxes). UniRig struggles with insufficient spatial continuity between sequentially generated bone chains. Specifically, the large gap between the terminal joint of completed chain and the expected starting position of the next chain creates initialization barriers, leading to incomplete skeletal structures with missing bone chains (highlighted in green boxes), particularly for characters with tails or wings. MagicAr- ticulate also suffers from similar shortcomings. RigNet, the other hand, frequently generates an excessive number joints. In contrast, our method consistently produces accurate, well-structured, and coherent skeletons that closely align with the shapes across diverse categories.
Skinning Comparison. Table 2 quantitatively compares baseline performance with and without incorporating the proposed geodesic-awar bone probability prediction module. The results demon- strate the effectiveness of our method in accurately identifying influential bones, reducing L1-norm errors, and preventing incorrect deformations during motion. Furthermore, Figure 5 presents qualitative comparison of the per-vertex L1-norm errors and predicted skinning weights. The method with the module shows its capability to produce more reliable skinning weights that closely match the ground truth and improve animation fidelity.
4.4 Ablation Studies
To validate the effectiveness of the key components of method, conducted series of ablation studies targeting: 1) the proposed connectivity- tokenization strategy, 2) the data augmentation strategy, and 3) the reward-guided DPO post-training strategy.
Effectiveness of the tokenization strategy. Table 3 compares the average token sequence length between our method against baselines. Compared to MagicArticulate, our method reduces sequence length by 26% by eliminating redundant parent joint encoding. For UniRig, despite similar compression efficiency, ignores skeletal topology and relies on manually designed heuristic rules to determine the connections between different bone chains, limiting generalization and resulting in suboptimal topology quality. In contrast, our method explicitly encodes skeletal topology and connectivity, ensuring better generalization.
Table 3: Comparison of different tok- enization strategics.
Method
Dataset
avg Tokens.
MagicArticulate UniRig Ours
201.00
An--LL2.
140.26
142.01
Effectiveness of the DPO post-training. We compared the post-trained model with the pre- trained model to assess the impact of reward-guided DPO. Table quantitatively demonstrates that DPO-enhanced results have achieve higher skeleton location accuracy. Additionally, the right side of Figure qualitatively highlights the topological connectivity improvements introduced by

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topology-awe aware reward function. Models without DPO often suffer from missing skeletal elements, such as the crab's 's right claw (highlighted in green boxes), or exhibit disorganized structures. such as wings, feet, and the crab's s legs (highlighted in orange boxes). In contrast, the full model produces more complete and structured results, aligning well with artistic preferences.
org
Effectiveness of the data augmentation. Figure 7 compares results with and without our data augmentation strategy on a character in walking pose not present in the dataset. Our full model generates skeletons that are better aligned with the character's shape, whereas the model trained without augmentation struggles. Furthermore, shown on the left side of Figure our model effectively handles cartoon characters and mn-the-wlld data, highlighting the augmentation's 's role in improving generalization to unseen poses and data types.
Figure Ablation study on data augmentation.
Conclusion
We present Auto-Connect, method that transforms static 3D meshes into animation-ready assets. By integrating connectivity-preseving tokenization, reward-guided fine-tuning, and geodesic-awand bone selection, our approach achieves exceptional rigging and skinning performance across diverse categories of 3D meshes. We believe Auto-Connect holds great potential to revolutionize 3D content creation, offering more efficient solution for digital artists by streamlining animation workflows. Limitations and future work are discussed in appendix C.
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 622021744 in part by the GJYC program of Guangzhou under Grant 2024D01J0081 and in part by the 7J program of Guangdong under Grant (023QN10X455 and in part by the Fundamental Research Funds for the Central Universities under Grant 2025ZYGX7R053.
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Appendix
In this appendix, we provide additional content to complement the main manuseript, including:
Further details of Auto-Connect (Section A).
Additional experimental results on rigging and skinning (Section B).
A discussion of the limitations our work and future works (Section C).
A More Details of Auto-Connect
A.1 More Details of Geodesic-aware Bone Probability Prediction Module
This module combining heterogeneous features, including vertex positions pr, vertex normal nos the coordinate of the bone start and end joints (piz. and the vertex-bone geometric distances computed from both raw and saplacian-smotther meshes. These features are processed by an three-layer MLP to predict bone influence probabilities:
b top k (MLLP((( (Pv. Rey Pbg. Poy. di di di donolb E {B))
(pu:
where dei and d'j denote distances after performing 5/10 laplacian smoothing iterations, respec- tively. The resulting selected bone set for each vertex used as the input to the skinning weight prediction module to compute the final skinning weights.
A.2 More Training Details
The rigging pre-training stage is conducted with a global batch size of 192, lasting days on the Art-XL22.0 dataset and 10 hours on the MR dataset. We use the Adam optimizer with a base learning rate 5 x 10-5, a weight decay of 0.001, and a linear warmup for the first 1,000 steps. For the DPO post-training stage, the optimizer remains unchanged, but the learning rate is reduced to x 10-6, and the coefficient for LSrT set to À = This stage performs epochs on 14k curated preference pairs. The RigFormer model consists of 24 layers with hidden dimension of 1024, and each transformer block incorporates a 16-head multi-heal self-attention mechanism.
For skinning weight prediction, following the baseline, we set A = 6 nearest bones and prune non-influential joints during ground truth construction. Training is conducted on x H20 GPUs with global batch size of 80, lasting day for the Art-XL2.0 dataset and 10 hours for the MR dataset. The geodesic-aware bone probability prediction module implemented as a three-layer MLP with hidden dimension of 256 and ReLU activation.
For the baselines MagicArticulate [38] and UniRig [39], utilize their publicly available pre-trained weights on the Art-X1.2.0 dataset for comparison since their training scripts are not provided. All other baselines [33, 46, 71] are retrained on the same datasets for fair comparison.
A.3 Animation Details
Auto-Can Auto-Connect provides an automated animation pipeline. The resulting animation-ready assets can be exported in standard formats such as FBX and GLB. These assets directly compatible with popular animation software like Blender [73] and Autodesk Maya [74], enabling digital artists to edit and refine them. Animation videos are included in the supplementary materials.
B Additional Experimental Results
B.1 More Rigging Result
We provide additional qualitative rigging results on both Articulation-XL. and ModelsResource datasets. As illustrated in Fig. 11, our method consistently generates high-quality skeletons, even
22

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Input image
Ours
Input image
Ours
Figure 8: More rigging results on mesh from in-the-wild images. We use off-the-shelf image-to- model Hunyuan3D 2.5 [3] to generated mesh from the input images. The rigging results demonstrate that our model has strong generalization to unseen data, achieving uttist-pproved skeleton quality and transforming static 3D meshes into animation-ready assets.
Table 5: Quantitative comparison skinning result on ModelsResource dataset. Models marked with were trained using our geodesic-awer aware bone probability prediction module.
Method
Dataset
Prec.
Rec.t
avg L1$
avg Dist.
RigNet
86.03%
79.03%
0.36
0.0058
RigNet"
87.85
80.11 %
0.33
0.0049
NeuroSkinning
86.24%
78.31% %
0.36
0.0057
MR
NeuroSkinning"
88.03
79.27%
0.33
0.0049
HeterSkinNet
87.31
78.99%
0.34
0.0052
HeterSkinNet'
89.09 %
79.45 %
0.28
0.0045
for complex cases. In contrast, the baseline methods produce suboptimal results that are not directly suitable for animation pipelines. Fig. 8 showcases more rigging results on Al-generated 3D data, further validating the robustness and effectiveness of our approach.
B.2 More Skinning Results
Table 5 presents a quantitative comparison baseline performance with and without incorporating this module on the ModelsResource dataset. The results demonstrate substantial improvements across all evaluation metrics, including higher precision, higher recall, lower LI- Ll-norm error, and reduced distance eиror. This highlights the effectiveness of approach in enhancing skinning accuracy. addition, Fig. 12 illustrates qualitative comparisons, where the integration of our module enables the baseline produce smoother and more realistic skin deformations, particularly in regions with complex geometries. These results further highlight the module's ability to accurately identify influence bones, leading to precise predictions of skinning weights with minimal LI ---orrmm error.
B.3 More Ablation Studies
More Ablation study on DPO post-training. Table 6 presents quantitative comparison of model performance with and without the proposed DPO post-training on the ModelsResource
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GT
Full model
w/o DPO
GT
Full mode!
w/o DPO
Figure 9: More qualitative ablation study on DPO post-training. Models trained without DPO often suffer from missing details-such as ears, tails, or fins (highlighted in green boxes)-or generate structural artifacts like crossing topologies (highlighted in orange boxes). In contrast, our full model effectively alleviates these issues, producing well-defined skeletons that better align with the artistic aesthetics expected by creators.
Table Quantitative ablation study DPO post-training on ModelsResource dataset.
Method
Dataset
CD-J21
CD-J2B]
CD-B2B
loUt
Prec.
Rec.
w/o DPO
3,426%
2.576%
2.164%
72.213%
73.700%
72.822%
MR
Ours
3.203%
2.436%
2.046%
73.108%
73.965
76.795%
dataset. The results clearly demonstrate that our reward-guided DPO post-training substantially improves the model's 's accuracy in skeleton localization, highlighting the effectiveness of the proposed approach. addition, Fig. 9 showcases additional qualitative evidence from ablation studies. Our full model produces more realistic topology connections and generates more complete skeletons, closely resembling the ground truth.
Input image
Input image
Figure 10: Some failure cases. Our method struggles with snake-like data, the input meshs are generated using Hunyuan3D 2.5 [3].
C Limitations and Future Work
While method achieves significant improvements in skeleton location accuracy, topological consistency, and skinning quality compared to prior approaches, still has some limitations. As shown in Fig. 10, the main drawback is the inability generalize well snake-like data, due the lack of such samples in the training dataset. Future work could address this by expanding the dataset with more snake-like examples by developing more robust data augmentation techniques. Another promising direction is leveraging multimodal input, such as text, image, and video, to allow user-friendly editing of rigging results.

---

GT
Ours
Magic-sptial
Magic-hier
UniRig
RigNet
Figure 11: More qualitative comparison of rigging results on Art-XL.2.0 (top) and ModelsResource (bottom). MagicArticulate often generates discontinuous skeletons (highlighted green boxes) or even fails entirely. UniRig tends to predict the termination token prematurely, leading to incomplete skeletal structures with missing bone chains (highlighted in orange boxes), and RigNet frequently produces overly dense joints, resulting in disorganized skeleton topologies. In contrast, our method reliably generates coherent, accurate, and well- sell-structured skeletons that closely conform to the shapes.
25

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HeterSkinNet
HeterSkinNet
Input skeleton
GT Skinning weights
Skinning weights
L1-norm errors
Skinning weights
L1-norm errors
NeuroSkinning"
NeuroSkinning
Input skeleton
GT Skinning weights
Skinning weights
L1-norm errors
Skinning weights
L1-norm errors
2.0
10
0.5
RigNet
RigNet
Input skeleton
GT Skinning weights
Skinning weights
L1-norm errors
Skinning weights
L1-norm errors
Figure 12: More qualitative comparison of skinning result on ModelsResource dataset. Models marked with were trained using our geodesic-awee -ware bone probability prediction module. By incorporating this module, the baseline method achieves lower L1-norm errors and more precise skinning weights, resulting in more accurate deformations during animation.
26


Reviews:
[{"review_id": "97whK5PIVm", "reviewer": "Reviewer_zoaB", "summary": "This 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.", "questions": "- 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?", "limitations": "Sufficiently discussed", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "After reading the rebuttal and the other reviews, I keep my original recommendation, as the main concerns are addressed.", "rebuttal": "We thank you for your constructive comments and appreciation, such as 'the DPO-based post-training refinement is intuitive and effective', 'the paper is well-written, easy to understand and follow', and 'the results of this paper are excellent, outperforming existing works both qualitatively and quantitatively'. We hope the following responses address your concerns, and we’re happy to provide further clarification if needed.\n\n\n> **Weaknesses 1: 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\n**Answer:** The goal of our DPO post-training is to improve the model’s joint localization accuracy and the fidelity of topological connections. To this end, we introduce a topology-aware reward composed of three metrics:    \n(1) Chamfer Distance (CD), which measures the spatial discrepancy between corresponding joints and bones of the predicted and ground truth skeletons;    \n(2) Tree Edit Distance (TED), which captures structural consistency by quantifying the minimal operations needed to transform one skeleton hierarchy into another;   \n(3) Hierarchical Jaccard Similarity (HJS), which ensures precise joint matching at each level of the hierarchy.   \nThis reward formulation is robust to minor joint count variations—such as the presence or absence of joints in the torso or fingers—while remaining sensitive to significant topological mismatches, such as incorrect limb connections.\n\n\n> **Weaknesses 2: 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\n**Answer:** Thank you for the careful evaluation of our work. We clarify that the vertex-to-bone distance in our method is computed based on two cases:  \nFor each bone, mesh vertices are categorized as visible or invisible. A vertex is considered visible if its perpendicular projection to the bone does not intersect the mesh surface; otherwise, it is invisible.   \n(1) For a visible vertices A, the distance is simply the perpendicular distance to the bone.   \n(2) For an invisible point B, we first find the nearest visible point A, then compute the geodesic distance along the mesh surface from B to A, plus the perpendicular distance from A to the bone.   \nSince the distance between B and A is explicitly computed along the mesh surface, we believe that \"geodesic\" is the correct term in the skinning section.\n\n\n\n> **Questions 1: Regarding the rigging problem, what is the current frontier in this area?**\n\n**Answer:** The current SOTA methods in automatic rigging include MagicArtiulate and UniRig, both of which serve as our baselines. However, they rely on heuristic post-processing to infer skeleton connectivity:    \n(1) MagicArtiulate predicts individual bones, where each bone is composed of two joints and is predicted as a pair of joints. To recover a full skeleton, it merges bones whose endpoints lie within a predefined spatial threshold. When the distance between bones exceeds this threshold, the connections fail, resulting in disconnected skeletons. Additionally, the method re-encode parent joints multiple times leading to longer sequences and slower inference times.     \n(2) UniRig predicts bone chains rather than individual bones, but similarly requires heuristic post-processing to connect these chains. Furthermore, since the end of one chain and the start of another are often distant in both spatial and sequential terms, the method frequently fails to initiate the next chain, resulting in an incomplete or fragmented skeleton.    \nIn contrast, our method employs special tokens that explicitly mark endpoints for each joint’s children and each hierarchical layer. This allows the transformer to directly encode both the skeleton topology and parent-child relationships within its representation. Consequently, the model gains stronger topology awareness, eliminates the need for heuristic post-processing, and can automatically infers joint connectivity, leading to more coherent and complete skeleton structures.\n\n\n\n> **Questions 2: How does the model perform on totally out-of-distribution cases?**\n\n**Answer:** Our model demonstrates strong robustness on out-of-distribution cases, thanks to three key design components:  \n(1) A point-cloud-based conditioning mechanism that provides strong geometric constraints;  \n(2) A carefully designed skeleton representation that generalizes well across diverse shapes;   \n(3) A topology-aware DPO post-training stage that further refines the model to produce structurally reasonable and semantically meaningful skeletons.   \nAs illustrated on the left side of Figure 6 in the main paper and Figure 8 of the appendix, our method effectively handles previously unseen, AI-generated character meshes, which often differ significantly from the artist-designed meshes in the training set due to domain gaps.\n\n\n> **Questions 3: How does it perform on strictly multi-body, piecewise-rigid objects?**\n\n**Answer:** Our method is designed for single-tree skeletons and does not currently support multi-body characters requiring multiple independent skeletons. Such configurations are beyond our current scope. However, for piecewise-rigid objects like weapons, a single joint typically suffices for effective rotation, and our method handles these cases well within its framework."}, {"review_id": "WpdHI9n6aw", "reviewer": "Reviewer_c5dz", "summary": "The 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.", "questions": "- 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?", "limitations": "See 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.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\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", "was_revised": true, "final_justification": "Questions have been answered in the rebuttal", "rebuttal": "We thank you for your constructive comments and appreciation, such as 'clear methodological difference to prior methods', 'overall consistent and significant improvements on the SOTA', and 'the probabilistic skinning weight prediction (k most probable instead of k nearest) is nice'. We hope the following responses address your concerns, and we’re happy to provide further clarification if needed.\n\n\n> **Recommendation: the supplemental material is extensive, yet it would be good to have a summary of limitations in the main paper**\n\n**Answer:** Thank you for the suggestion. In the revision, we will include a comprehensive discussion of limitations and potential future work directly in the main paper.\n\n\n\n> **Weaknesses 1: 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\n**Answer:** The two steps cannot be merged, and this has been empirically validated in both large language models (LLMs) and vision-language models (VLMs). The post-training stage is designed to fine-tune a well-trained base model. Our topology-aware reward signals—such as Chamfer Distance, Tree Edit Distance, and Hierarchical Jaccard Similarity—only become informative and effective when the model's predictions are already reasonably close to valid skeletons. In the early stages of training, when the model outputs are still noisy or unstructured, these rewards cannot provide stable guidance and may even mislead learning. Therefore, a good pre-trained model is essential before applying these rewards effectively during the post-training phase.\n\n\n> **Weaknesses 2: are snake like shapes the only limitation?**\n\n**Answer:** Snake like shapes are not our method’s only limitation. Our method may also struggle with characters that have complex decorations or accessories that are not present in the training data, sometimes resulting in unreasonable skeletons for those regions. However, this is a common limitation across all existing methods—baseline approaches also fail in such cases. Resolving this issue requires more comprehensive and diverse training data covering such complex interactions, which is an important direction for future work.\n\n> **Weaknesses 3: 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\n**Answer:** It can rotate with the end joints. Skinning weights are assigned to joints, including end joints, which influence the motion of terminal segments. Specifically, for the ant shown in Figure 11 of the appendix, the last joint affects the movement of the terminal limb segment, allowing it to move accordingly. This behavior is analogous to the spider case demonstrated in the supplementary video.\n\n\n> **Minor: \"160 Shape-conditioned generation. we randomly samp\" -> We**\n\n**Answer:** Thank you for pointing out this typo. We will correct it in the revision.\n\n\n\n> **Questions: 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\n**Answer:** In fact, we performed a qualitative comparison. The shark in Figure 6 of the main paper is identical to the shark in Figure 7 of the RigAnything paper. Our rigging results are more comprehensive, for example, we successfully generate the skeleton for the shark’s dorsal fin, which RigAnything fails to capture.    \nFurthermore, RigAnything relies on an additional connectivity module to predict joint connections, which increases memory consumption and inference time. In contrast, our method naturally incorporates joint connectivity during inference through a connectivity-preserving tokenization design, eliminating the need for additional modules and improving efficiency.    \n\n\n> **Limitations: See 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.**\n\n**Answer:** In the revision, we will provide a more comprehensive evaluation of limitations by highlighting the worst-performing test cases—particularly those involving complex accessories or significant domain shifts. We will also include failure cases from MagicArticulate, especially on meshes with very coarse surfaces (e.g., 3D reconstruction results), to offer a more informative evaluation."}, {"review_id": "vVI1PGziC0", "reviewer": "Reviewer_8uXR", "summary": "This 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.", "questions": "1. **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*?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": false, "final_justification": "", "rebuttal": "We thank you for your constructive comments and appreciation, such as 'well-motivated and technically sound', 'superior performance', and 'the proposed skinning strategy provides performance gains'. We hope the following responses address your concerns, and we’re happy to provide further clarification if needed.\n\n\n> **Weaknesses 1: 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\n**Answer:** Below, we first describe the tokenization strategies used in existing baselines and point out their limitations, then clarify how our comparative experiments already provide an effective evaluation of different tokenization strategies.   \n\n**Tokenization strategies description**  \n(1) MagicArticulate employs a bone-wise tokenization strategy with two sequence ordering variants:   \n* Spatial sequence ordering: Bones are sorted based on the spatial location of their joints, first by the lower joint, then by the higher one.    \n* Hierarchical sequence ordering: Bones are ordered based on the skeleton hierarchy. Starting from the root joint, bones connected to it are ordered by ascending child joint index. For subsequent layers, bones are grouped by their immediate parent and sorted within each group by child joint index, the groups themselves are processed in ascending parent joint order.     \n\nBoth variants requires heuristic post-processing to determine connections between bones. Specifically, it merges bones whose endpoints lie within a predefined threshold. If bones fall outside this threshold, the connections fail, leading to disconnected skeletons. Additionally, the method re-encode parent joints multiple times leading to longer sequences and slower inference times.     \n\n(2) UniRig decomposes the skeleton into linear bone chains via Depth-First Search (DFS), each prefixed with a <branch token\\>. However, it similarly relies on heuristic post-processing to connect these chains. Moreover, because the end of one chain and the start of another are often spatially and sequentially distant, the method frequently fails to initiate subsequent chains, resulting in an incomplete or fragmented skeleton.  \n\n**Tokenization strategies comparison**    \nIn fact, our experiments include a comparison of the proposed tokenization strategy. The main difference between our pre-training stage and those of MagicArticulate and UniRig lies in the tokenization approach, while the model architecture remains a standard transformer. This means that comparing our w/o DPO (stage 1) rigging results directly against MagicArticulate and UniRig effectively serves as a comparison of the tokenization strategy. The results without the DPO stage are reported in Table 4 of the main paper, and baseline results is reported in Table 1 of the main paper. For clarity, we summarize them below:\n\n| Method        | Dataset    | CD-J2J↓ | CD-J2B↓ | CD-B2B↓ | IoU↑    | Prec.↑  | Rec.↑   |\n|:--------|:----------|:-------|:-------|:-------|:------|:------|:-------|\n| Magic-hier   | Art-XL2.0 | 3.435% | 2.757% | 2.393% | 76.364% | 78.121% | 77.567% |\n| UniRig       | Art-XL2.0 | 3.232% | 2.540% | 2.124% | 75.571% | 77.334% | 77.323% |\n| Magic-spatial| Art-XL2.0 | 3.041% | 2.479% | 2.099% | 78.675% | 80.026% | 80.085% |\n| **Ours stage 1** | **Art-XL2.0** | **2.695%** | **2.109%** | **1.750%** | **82.183%** | **84.234%** | **82.734%** |\n\n\n\n \n> **Weaknesses 2: 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\n**Answer:** In fact, the right side of Figure 7 in the main paper labeled 'w/o Data aug.' refers to the setting without data augmentation. Additionally, we provide a quantitative ablation on the effect of data augmentation in the table below:\n\n| Method   | Dataset    | CD-J2J↓ | CD-J2B↓ | CD-B2B↓ | IoU↑   | Prec.↑ | Rec.↑   |\n|:--------|:----------|:-------|:-------|:-------|:------|:------|:-------|\n| Ours w/o Data aug. | Art-XL2.0  | 2.834%  | 2.351%  | 1.827%  | 81.017%| 82.835%| 81.393% |\n| **Ours Full model**     | **Art-XL2.0**  | **2.572%** | **2.030%** | **1.683%** | **82.806%** | **85.254%** | **82.918%** |\n\n\n> **Weaknesses 3: 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\n**Answer:** Thanks for this comment, we will incorporate this introduction into the revision. Specifically, DPO is a method for post-training using preference data without explicitly learning a reward model. It operates by directly optimizing the policy to align with human preferences through a loss function derived from preference comparisons. The preference pairs consist of different outputs generated from the same input, paired and labeled as \"good\" and \"bad\" either manually or based on reward scores. The reference policy, typically the pre-trained model, serves as a fixed baseline to prevent excessive deviation, while the optimized policy is fine-tuned to increase the likelihood of preferred responses. In our case, both the reference and optimized policies are initialized from the same pre-trained model obtained after stage 1. The reference policy is frozen and serves as a stable baseline to prevent excessive deviation, while the optimized policy remains trainable and is further fine-tuned using both DPO and supervised fine-tuning (SFT) losses to generate more plausible and accurate skeletons.\n\n\n\n> **Weaknesses 4: An explanation of how skinning weights are visualized in Figure 5 would be helpful for readers.**\n\n**Answer:** Thanks for this comment, we will update in the revision. The skinning weight visualization follows a three-step process:    \n(1) Color assignment: each joint is randomly assigned a unique color from a predefined palette.  \n(2) Weighted color blending: vertex colors are computed as a linear combination of joint colors, proportionally weighted by each vertex's skinning weights, donated as $\\text{Color}(v_i) = \\sum_{j=1}^{J} w_{i,j} \\cdot \\text{Color}(b_j)$, where $J$ is the number of joints, $w_{i,j}$ is the skinning weight of vertex $v_i$ to joint $b_j$, and $\\text{Color}(b_j)$  is the color assigned to joint $b_j$.   \n(3) Mesh rendering: the computed vertex colors are applied to the mesh and visualized using smooth shading in Blender.  \n\n\n> **Weaknesses 5: In the method section, many abbreviations (e.g., DPO, SFT, CD-J2J, CD-J2B, CD-B2B) should be defined upon their first appearance.**\n\n**Answer:** Thanks for this comment, we will update in the revision.\n\n\n\n> **Questions 1: 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?**\n\n**Answer:** The Bone Probability Prediction and Skinning Weight Prediction modules operate sequentially. At each step, the Bone Probability Prediction Module first estimates the probability that each bone influences a given vertex. Then, the top-$k$ bones with the highest probabilities are selected. These selected bones are then passed to the Skinning Weight Prediction Module, which predicts the skinning weights between the given vertex and this subset of bones.\n\n\n\n> **Questions 2: 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\n**Answer:** These two losses are applied to different modules and operate independently. The Binary Cross-Entropy (BCE) loss is applied to the Bone Probability Prediction Module, guiding it to correctly identify the set of bones that potentially influence the given vertex. Specifically, as described in lines 226–229 of the main paper, for each vertex, influencing bones are labeled as 1 and non-influencing bones as 0. We binarize the predicted probabilities by selecting the top-$k$ highest values as 1, with others set to 0, and compute the BCE loss against these ground-truth binary labels.\nThe Kullback-Leibler (bones) Divergence loss is used in the Skinning Weight Prediction Module to ensure accurate prediction of the skinning weights between vertices and the given set of bones.  \nThese two losses are computed on different variables, BCE loss is calculated between the predicted probabilities $b$ and the ground truth labels ${\\hat b}$, while the KL loss is computed between the predicted skinning weights $w_{ij}$ and the ground truth skinning weights ${\\hat w}_{ij}$.\n\n\n\n\n> **Questions 3: 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\n**Answer:** We determine shared joints by traversing both the predicted and ground-truth skeletons using our tokenization order. During this traversal, we assign new names to joints based on their positions in the hierarchy: we perform a BFS traversal of the skeleton, and within each layer, joints are sorted by their height along the z-axis. Joints with matching names after this process are considered shared."}, {"review_id": "49weTT7JaD", "reviewer": "Reviewer_LNzV", "summary": "The 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.", "questions": "Overall, 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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thank the authors for their responses, which have effectively addressed my concerns. However, as the results of the ablation study show, data augmentation has brought a significant improvement to the proposed method. Considering the effect brought in post-training, I increase the rating to Borderline Accept.", "rebuttal": "We thank you for your constructive comments and appreciation, such as 'the DPO-based post-training refinement is intuitive and effective', 'the paper is well-written', and 'the results of this paper are excellent'. We hope the following responses address your concerns, and we’re happy to provide further clarification if needed.\n\n\n> **Weaknesses 1 & Questions 1: 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. Explain why the results in this paper are inconsistent with the original MagicArticulate paper.**\n\n**Answer:**  This is due to different versions of the Articulation-XL dataset. The original MagicArticulate paper used Articulation-XL 1.0 (contains 33k samples, with 31.4k for training and 1.6k for testing), whereas our results are evaluated on the more recent and larger Articulation-XL 2.0 (contains 48k samples, with 46k for training and 2k for testing). The results reported in Table 1 of the main paper were obtained using the official MagicArticulate code and checkpoint on the Articulation-XL 2.0 test set. The performance of MagicArticulate on Articulation-XL 2.0 is also documented in the README of their repo. We ensured a fair comparison by evaluating all baselines and our method on the same settings on the Articulation-XL 2.0 dataset.\n\n\n\n> **Weaknesses 2 & Questions 3: 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. Supplement additional ablation results to thoroughly validate the effectiveness of the proposed modules.**    \n\n**Answer:** In fact, our experiments include a comparison of the proposed tokenization strategy. The main difference between our pre-training stage and those of MagicArticulate and UniRig lies in the tokenization approach, while the model architecture remains a standard transformer. This means that comparing our w/o DPO (stage 1) rigging results directly against MagicArticulate and UniRig effectively serves as a comparison of the tokenization strategy. The results without the DPO stage are reported in Table 4 of the main paper, and baseline results is reported in Table 1 of the main paper. For clarity, we summarize them in Table 1 below:\n\n**Table 1: Comparison on different tokenization methods**   \n| Method        | Dataset    | CD-J2J↓ | CD-J2B↓ | CD-B2B↓ | IoU↑    | Prec.↑  | Rec.↑   |\n|:--------|:----------|:-------|:-------|:-------|:------|:------|:-------|\n| Magic-hier   | Art-XL2.0 | 3.435% | 2.757% | 2.393% | 76.364% | 78.121% | 77.567% |\n| UniRig       | Art-XL2.0 | 3.232% | 2.540% | 2.124% | 75.571% | 77.334% | 77.323% |\n| Magic-spatial| Art-XL2.0 | 3.041% | 2.479% | 2.099% | 78.675% | 80.026% | 80.085% |\n| **Ours stage 1** | **Art-XL2.0** | **2.695%** | **2.109%** | **1.750%** | **82.183%** | **84.234%** | **82.734%** |\n\n\nAdditionally, we provide a quantitative ablation on the effect of data augmentation in Table 2 below:      \n**Table 2: Ablation on the effect of data augmentation**   \n| Method   | Dataset    | CD-J2J↓ | CD-J2B↓ | CD-B2B↓ | IoU↑   | Prec.↑ | Rec.↑   |\n|:--------|:----------|:-------|:-------|:-------|:------|:------|:-------|\n| Ours w/o Data aug. | Art-XL2.0  | 2.834%  | 2.351%  | 1.827%  | 81.017%| 82.835%| 81.393% |\n| **Ours Full model**    | **Art-XL2.0**  | **2.572%** | **2.030%** | **1.683%** | **82.806%** | **85.254%** | **82.918%** |\n\n\n\n> **Weaknesses 3: Table 4 indicates that DPO provides only limited quantitative gains. Does pre-training alone achieve results far superior to the current SOTA?**\n\n**Answer:** Yes, our pre-trained model already significantly outperforms prior SOTA methods, as shown in Table 1 in the answer for Weaknesses 2 & Questions 3. While DPO brings limited improvements in quantitative metrics, which primarily reflect joint localization accuracy, its main contribution lies in enhancing connectivity fidelity, as shown in Figure 6 right. This leads to more coherent and topologically accurate skeletons that better match the expectations of designers and technical artists—an improvement not fully captured by standard metrics.\n\n\n> **Weaknesses 4 & Questions 2: The main improvement of the token is the addition of <E2\\> compared to other BFS skeletons. 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.**  \n\n**Answer:** Below, we detail the tokenization strategies used in baselines and point out their limitations, then highlight the advantages of our approach. Table 3 summarizes the key differences.\n\n**Similarities**  \nAll tokenization strategies aim to serialize the skeleton into a sequence for Transformer-based modeling.\n\n**Differences**  \nThe key differences lie in the sequence ordering strategy and the reliance on heuristic post-processing to recover connectivity. These are summarized in Table 3 below:    \n**Table 3: Summary of differences in tokenization strategies.** T: number of bones; S: number of branches; M: number of special tokens (typically close to S$\\times$4).\n| Method   | Number of tokens  | Order  | Post-Processing Required | \n|:--------|:----------|:-------|:-------|\n| MagicArticulate    | 6T | Spatial/Hierarchical | Yes | \n| Unrig | 3T + S$\\times$4| DFS + <branch token\\>| Yes  |\n| Ours  | 3T + M | BFS + <special token\\> | No | \n\n**Limitations of Baselines**  \n(1) MagicArticulate employs a bone-wise tokenization strategy with two sequence ordering variants:   \n* Spatial sequence ordering: Bones are sorted based on the spatial location of their joints, first by the lower joint, then by the higher one.   \n* Hierarchical sequence ordering: Bones are ordered based on the skeleton hierarchy. Starting from the root joint, bones connected to it are ordered by ascending child joint index. For subsequent layers, bones are grouped by their immediate parent and sorted within each group by child joint index; the groups themselves are processed in ascending parent joint order.    \n\nBoth variants requires heuristic post-processing to determine connections between bones. Specifically, it merges bones whose endpoints lie within a predefined threshold. If bones fall outside this threshold, the connections fail, leading to disconnected skeletons. Additionally, the method re-encode parent joints multiple times, leading to longer sequences and slower inference times.     \n\n(2) UniRig decomposes the skeleton into linear bone chains via Depth-First Search (DFS), each prefixed with a <branch token\\>. However, it similarly relies on heuristic post-processing to connect these chains. Moreover, because the end of one chain and the start of another are often spatially and sequentially distant, the method frequently fails to initiate subsequent chains, resulting in an incomplete or fragmented skeleton.\n\n**Merits of Our Method**  \nThe main improvement is not merely the addition of <E2\\> compared to other BFS skeletons, but by introducing special tokens to explicitly mark endpoints for each joint’s children and each hierarchical layer. This allows the transformer to directly encode both the skeleton topology and parent-child relationships within its representation. Consequently, the model gains stronger topology awareness, eliminates the need for heuristic post-processing, and can automatically infer joint connectivity, leading to more coherent and complete skeleton structures.\n\n\n\n\n\n\n\n> **Weaknesses 5: The rigging results of hands in Fig. 4 and Fig. 9 seem contradictory.**\n\n**Answer:** The rigging results in Figure 4 and Figure 9 differ because the two hand meshes are structurally distinct, and their ground-truth skeletons are also different.\n\n\n\n\n\n> **Weaknesses 6: Performance on in-the-wild data is not good. For instance, in the first row of Figure 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\n**Answer:** Our DPO stage does not encourage skeleton expansion but rather promotes correct joint connectivity, as evidenced by both quantitative (Table 4 in the main paper) and qualitative (Figure 6 right in the main paper) improvements.   \nThe issues in Figure 6 are unrelated to DPO. They arise because our training data lacks meshes with character-object interactions (e.g., holding weapons), making it difficult for the model to distinguish between body parts and accessories. Additionally, these meshes are AI-generated and therefore exhibit inherent domain gaps compared to the artist-designed meshes in our training set. This is a common limitation across all existing methods, including baselines. Addressing it will require more diverse training data with such interactions and semantic annotations.\n\n\n> **Questions 4: As 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\n**Answer:** Thank you for the insightful observation. We agree that incorporating semantic information—such as identifying which regions should or should not be rigged—could intuitively improve skeleton prediction, especially in scenarios involving human-object interactions. While our current framework relies primarily on geometric and topological cues, extending it with richer semantic understanding is a valuable and promising direction for future work."}]

Author rebuttal:

Meta-review: (a) The paper proposed a three‑stage rigging+skinning system: 1) a connectivity‑preserving tokenization that encodes skeleton topology directly into a transformer’s output sequence; 2) a post‑training step using Direct Preference Optimization (DPO) with a topology‑aware reward (CD/TED/HJS) to improve joint localization and hierarchical correctness; 3) a probabilistic top‑k skinning layer informed by (mesh‑)geodesic distances. Experiments and extensive supplementals show strong topology fidelity and visual quality over recent baselines.

(b) + Clear architectural novelty in tokenization (topology encoded up front) and principled post‑training with a topology‑aware reward.
     + Showed competitive quantitative results, strong visuals, and evidence of OOD robustness.

(c) - Reviewers pointed out the tokenization ablation under‑powered; limited intuition for DPO/reward design; several definitions (BCE vs KL, “shared joints”) and visualization details were terse.

(d)(e) All reviewers provide positive rating and tend to accept this paper, with final score of {4,5,4,4}. All correctness‑critical issues are addressed, and remaining items are presentation/coverage and are camera‑ready fixable. For this reason, AC agree with the acceptance decision.
Decision: Accept (poster)