Stroke3D: Lifting 2D strokes into rigged 3D model via latent diffusion models
Authors: Ruisi Zhao
Haoren Zheng
Zongxin Yang
Hehe Fan
Yi Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Diffusion model
3D generation
Graph diffusion
Primary area: generative models

Abstract: Rigged 3D assets are fundamental to 3D deformation and animation. However, existing 3D generation methods face challenges in generating animatable geometry, while rigging techniques lack fine-grained structural control over skeleton creation. To address these limitations, we introduce Stroke3D, a novel framework that directly generates rigged meshes from user inputs: 2D drawn strokes and a descriptive text prompt. Our approach pioneers a two-stage pipeline that separates the generation into: 1) Controllable Skeleton Generation, we employ the Skeletal Graph VAE Sk-VAE to encode the skeleton's graph structure into a latent space, where the Skeletal Graph DiT Sk-DiT generates a skeletal embedding. The generation process is conditioned on both the text for semantics and the 2D strokes for explicit structural control, with the VAE's decoder reconstructing the final high-quality 3D skeleton; and 2) Enhanced Mesh Synthesis via TextuRig and SKA-DPO, where we then synthesize a textured mesh conditioned on the generated skeleton. For this stage, we first enhance an existing skeleton-to-mesh model by augmenting its training data with TextuRig—a dataset of textured and rigged meshes with captions, curated from Objaverse-XL. Additionally, we employ a preference optimization strategy, SKA-DPO, guided by a skeleton-mesh alignment score, to further improve geometric fidelity. Together, our framework enables a more intuitive workflow for creating ready-to-animate 3D content. To the best of our knowledge, our work is the first to generate rigged 3D meshes conditioned on user-drawn 2D strokes. Extensive experiments demonstrate that Stroke3D produces plausible skeletons and high-quality meshes.

Published as a conference paper at ICLR 2026
STROKE3D: LIFTING 2D STROKES INTO RIGGED 3D MODEL VIA LATENT DIFFUSION MODELS
Ruisi Zhao', Haoren Zheng', Zongxin Yang,, Hehe Fan', Yi Yang ReeLER, CCAI, Zhejiang University DDMM, HMS, Harvard University {zhaors00, zhenghaoren, hehefan, yangyics] edu. Zongxin.Yang@hnss hawaangggggnnsaaad..eee....
ABSTRACT
Rigged 3D assets are fundamental to 3D deformation and animation. However, existing 3D generation methods face challenges in generating animatable geom- etry, while rigging techniques lack fine-grained structural control over skeleton creation. To address these limitations, we introduce Stroke3D, a novel frame- work that directly generates rigged meshes from user inputs: 2D drawn strokes and descriptive text prompt. Our approach pioneers a two-stage pipeline that separates the generation into: 1) Controllable Skeleton Generation, we employ the Skeletal Graph VAE (SK-VAE) to encode the skeleton's 's graph structure into a latent space, where the Skeletal Graph DiT (Sk-DiT) generates a skeletal embed- ding. The generation process is conditioned 2D strokes for explicit structural control, with the VAE's decoder reconstructing the final high-quality 3D skeleton; and 2) Enhanced Mesh Synthesis via Tex- tuRig and SKA-DPO, where we then synthesize a textured mesh conditioned on the generated skeleton. For this stage, we first enhance existing skeleton-to- mesh model by augmenting its training data with TextuRig-a dataset of textured and rigged meshes with captions, curated from Ob Additionally, we employ a preference optimization strategy, SKA-DPO, guided by a skeleton-mest meshh alignment score, to further improve geometric fidelity. Together, our framework enables more intuitive workflow for creating readaato-animate 3D content. To the best of our knowledge, our work is the first to generate rigged 3D meshes conditioned on user-drawn 2D strokes. Extensive experiments demonstrate that Stroke3D produces plausible skeletons and high-quality meshes.
both the text for semantics and the
INTRODUCTION
Rigged 3D assets (Au et al. 2008 Xu et al. 2019) are fundamental to realistic 3D deformation and animation (Zhao et al. 2024 2025). They are widely applied in AR/VR, robotics simulation, and the film industry. Professional 3D creation software, such as Blender (Blender 2025). provides powerful tools for precise geometry creation and animation control. However, the steep learning curve and complexity of these platforms often present significant barrier for beginners. As result, numerous generative methods have emerged that offer more user-friendly alternatives to these tools.
Although existing methods achieve notable results in 3D asset generation, they still face two key limitations. 1) Difficulty in generating animatable geometry. Numerous studies (Shi er al.) 2023 Zhang et al. 2024 Lai et al.) 2025) focus on generating 3D representations, but these methods typically yield static geometries that lack the skeletal hierarchy required for animation. One line of work, such as SKDream (Xu al.) (202).. which generates animatable meshes conditioned on skeletons, is limited by the scarcity of datasets with high-quality, paired skeleton and textured-mesh annotations. 2) Limited structural control over the skeleton creation. Current methods for skeleton generation (Song et al., 2025b Deng et al.) 2025 Zhang et al. 2025) typically employ end-to- end -ees--to-skeleton paradigm, conditioned on large skeleton-mesh datasets. However, the absence
"Corresponding author

---

Published as a conference paper at ICLR 2026
T
Adding Strokes
Homan
"A man is running the ground,"
Anbhingg
Synthhnnk
Creatonee
"A dinosaur is poired to spring into action."
Modily Joint-Pos
Animal
Down-tal
Syntheala
Creations
Animation
"a bird Clying in sky"
Synthssss
Plant
tree stansing
tall."
User-drawn
Language
Skeleton-Mesh
More
Strakes
Instruction
Genaration
Application
Figure 1: We present Stroke3D (SSection 3). novel framework that generates rigged 3D meshes
from user-drawn strokes and language instructions. We show versatile downstream applications, including generation from different viewpoints, structural editing by adding strokes or modifying joint positions, and final animation. Skeleton color represents the depth in 3D space.
of explicit structural constraints leads to skeletons appearing in unnecessary locations while being absent where they are critically needed, resulting unpredictable results.
Considering these limitations, we introduce Stroke3D, novel framework that enables users to gen- crate rigged 3D meshes simply by drawing 2D strokes and providing a text prompt. Unlike previous methods that generate the mesh first and then rig it, our method pioneers skeleton-driven work- flow, empowering non- non-professional users to effortlessly generate ready-to-animate 3D assets. For controllable skeleton generation, tent graph diffusion model (Ho et al. 2020 Song et al. 2020 202T) to synthesize a complete 3D introduce TextuRig,
method represents the skeleton graph and directs a la-
skeleton conditioned on user input. To facilitate the mesh generation, curated dataset from Objaverse-Xi XI. (Deitke et al. 2023). We leverage it to augment training data for skeleton-to- to-tesh method SKDream (Xu et al., (025) to create the final mesh. We then apply SKA- DPO, a custom DPO (Rafailov et al.) 2023) strategy that significantly enhances the final mesh's 's geometric quality and correspondence the skeleton.
More specifically, Stroke3D builds upon Skeletal Graph VAE (Kipf & Welling 2016 Kingma & Welling 2013) VSE- that encodes the skeletal graph structure into latent space. To generate skeleton embeddings, we introduce Skeletal Graph Diffusion Transformer (Peebles & Xie 2023) (SK-DiT) that operates within this latent space. We employ TransformerConv (Shi et al. 2020) to perform self-attention (Vaswani et al. (017), which is suited for graph-structured data. Furthermore, our Sk-DiT incorporates cross-attention to integrate semantic guidance from the text. For structural conditioning, the model is guided by concatenating features derived from the user-drawn strokes with the latent noise. To simulate the stroke input during training, we simulate stroke-like data by applying perturbations to 2D projections of 3D skeletons, which mimics the imprecision inherent in hand-drawn strokes. Finally, the VAE's decoder reconstructs the 3D skeleton from the generated embedding, ensuring that the output is both realistic and aligned with the user's specified intent.
For the mesh synthesis stage, we present TextuRig, a dataset comprising textured, rigged 3D) models with detailed captions. Our curation process builds upon the rigged subset of Objaverse-XX -L (Deitke eet al. 2023) identified by UniRig (Zhang et al., 2025). However, observing that the existing data UniRig often lacks essential texture, we execute specialized re-processing pipeline. This in- volved a filtering step to strictly verify the presence of texture maps or vertex colors, followed by re-captioning each model via Gemini (Team al. 2023) to provide rich textual context. We utilize TextuRig to augment the training data for the Srerm model, thereby enhancing its generative robustness. To further improve geometric fidelity, we apply SKA-DPO, a tailored preference opti- mization strategy. This method leverages the SKA Score (SKeleton Alignment Score) (Xu et al. 2025) to evaluate skeleton-men mesh alignment, constructing preference pairs for Direct Preference Op- timization (DPO) (Rafailov et al. 2023) that guide the model toward superior structural coherence.
Combining the designs mentioned above, Stroke3D achieves superior performance in generating high-quality skeleton-mest pairs. Furthermore, the generations be directly processed by mature and standard automatic skinning tools (c.g., in Blender) to obtain fully rigged assets. Quantitative evaluations the ArticArtieulatee and SKDream benchmarks further demonstrate superior
2

---

Published as a conference paper at ICLR 2026
Skeleton
Prompt: Diven se: profection ixages about the saza 30 abfect
Randar
Caption
dl descriptions
Se Bender
GPT/
into one cepriions
Gemini
Answee: waie La standing calmiy."
3D Model
20 Orthogonal Projectione
Language
We render the skeleton and its corresponding mesh together into orthogonal projections using pyrender or Blender. This provides the necessary visual context to represent the object's identity and pose clearly. These views are then fed into Vision-Language Model (VLM) to generate detailed descriptions of the object's identity and pose.
Figure 2: Skeleton-Caption Pipeline ($Section 3.1
performance on skeleton and mesh generation, respectively. Stroke3D excels skeleton generation, achieving the lowest Chamfer Distance on most metrics. In particular, our method improves the /ean SKA score by nearly 10 points over the SKDream baseline. In summary, our main contributions are as follows:
To the best of our knowledge, user-drawn 2D strokes and text prompts. Our Stroke3D pioneers a skeleton-firy generation pipeline that utilizes a latent graph diffusion model to synthesize 3D skeleton.
the first to generate rigged 3D meshes directly from
For mesh generation, we introduce TextuRig, dataset of textured and rigged 3D meshes with captions, which we leverage to improve the quality of generated textured meshes. Additionally, we apply SKA-DPO to further refine the model's geometric fidelity and alignment.
We demonstrate through extensive experiments that Stroke3D achieves superior performance. Our method consistently outperforms existing approaches in generating plausible skeletons and high- fidelity rigged meshes that align with user inputs.
2 RELATED WORK
2021 demonstrate remarkable success in vision generation [Rombach et al. 2022 Wan et al. 2025 Labs 2024 Wu et al. 2024a b 2025b). This success also catalyzes significant progress the field of 3D generation (Li et al.
3D Generation. Diffusion models (Ho et al.
2020 Song et al. 2020
2023b Zhou et al. 2025 Li al. 2026). In early work, DreamFusion (Poole et al. 2022) is the first to use SDS loss to optimize a NeRF (Mildenhall al. 2021 representation. Subsequently. many methods adopt multi-stage approach (Li et 2003a Xu et al. 2024). MVDream (Shi et al. 2023)
first generates multiple views, which are then used by a reconstruction model such as LRM (Hong
et al. 2023) to create the final 3D asset. More recent works (Xiang et al.) 2025 Lai et al.) 2025
such as CLAY (Zhang et al.) 2024). use a transformer-basee 3D diffusion framework (Peebles & Xic 2023) and various conditions such as images and point clouds to generate 3D representations.
Skeleton Generation. Mesh rigging (Au et al., 2008 Xu et al., 2019 Baran & Popović 2007 [i et al., (021) represents long-standing area of exploration. Early works such RigNet (Xu ctt al. 2020) predict skeletons directly from the 3D mesh. SKDream (Xu et al. 2025) uses methods based on MCF (Talliasacchi al. 2012) to generate curve skeletons from meshes. More recently, a new trend emerges with methods (Song et al. 2025b Liu et al., 2025 Deng et al., 2025 Zhang et al. 2025) that propose regressgee skeleton generation, which is supported by the large-scale skeleton-mesh datasets, enabling more generalizable solutions for the community. However, decou- pling skeleton creation from geometry generation is shared limitation of these methods, resulting in
a lack of explicit control over the final rigged structure and often leading to unpredictable outcomes.
RL. in Diffusion. The remarkable advancements in Large Language Models (LLMs)) (Bai et al. 2022a by Lee et al. (2023) and Vision-Language Models (VLMs) (Sun et a0.3 Yu al. 2024a b) demonstrate the power of Reinforcement Learning. Inspired by this success, diffusion models (Li et al. 2025a Ye et al. 2024 Li et al. 2025b; Wu et al. 2025a) leverage RL tools to achieve higher
quality generation with human preference. DiffusionDPO ( (Wallace et al. 2024 optimizes diffusion models using human preference data, and DPOK (Fan et al.) (023) integrates policy optimization with Kullback-Leible (KI.) regularization. In 3D generation, Carve3I) (Xie et al. 2024 leverages
3

---

Published as a conference paper at ICLR 2026
Skeleton Generation
BM-VE
9k.OIT
Sk-WAE Decoder
HIF
Suule
Friyn & XY' Coret
Sk-OIf
Grpph
BI-VAE Encoder
OchlCzer
Scar, ShV1
Encoder
Edge
geceed.
Crossidaation
Largaage
Thma Shoee Node Generation
Tedd
Layedtoonn
Transforaa
Alge
Ooo Edge
Trenaforattarr/
Shoon
Scate
Yr,B
Condrioe
Deret Preferencs
86A Stors
Layehtcon
High
LOW
XY Quaed
4 Sbake Crb
Timeetop
(a) Pipeline Overview
(b) Module Details
Figure 3: Overview of Stroke3D (SSection 3). During the training phase, SK-VAE encodes skele- ton graph into a latent space. Subsequently, Sk-DiT is trained to generate these latent embeddings, conditioned on the corresponding 2D strokes and text prompt. After training with TextuRig, we leverage SKA-DPO to further refine SKDream with a skeleton-mesh alignment reward signal. The right side illustrates the implementation details of our models.
multi-view consistency reward signal to finetune the model, and DreamDPO (Zhou al. 2025) provides automated evaluations by VLM for its DPO framework. In the domain of rigging, Auto- Connect (Guo al. 2025) demonstrates that its stragety can achieve improved rigging quality.
METHOD
As illustrated in Fig. proposed framework, Stroke3D, is designed translate user-drawn 2D strokes and text prompts into rigged 3D meshes. The implementation process begins with Data Preparation (SSection 3.1.. where we provide descriptive text labels for each 3D skeleton, curate the TextuRig dataset by sourcing assets from Objaverse-XL. (Deitke et al., 2023) and design a can- vas tool to record skeleton topology through user-drawn strokes during inference. Subsequently, a Skeletal Graph Variational Autoencoder ($Section 3.2) is trained to learn continuous latent space for these skeletal graphs. Operating within latent space, Skeletal Graph Diffusion Trans- former (SSection 3.3) then generates skeletal embedding that adheres to the user's input. The final stage is Enhanced Mesh Synthesis via TextuRig and SKA-DPO (SSection 3.4). Here, we first leverage the TextuRig dataset to augment the SKDream (Xu et al. 2025). We then apply SKA-DPO, which uses the pelleton-mesle alignment quality as reward signal to achieve DPO (Rafailov et al. 2023). This process ensures that the final generated mesh achieves high structural coherence.
3.1
DATA PREPARATION
Our data preparation pipeline is composed of three critical stages: 1) enriching existing skeleton datasets with detailed semantic captions, 2) curating new dataset, which term TextuRig, fa- cilitate high-quality skeleton-to-men mesh generation, and 3) recording skeleton through designed canvas tool during inference.
Skeleton Caption. Existing datasets for skeleton generation (Xu et al. ten provide only coarse category labels, such as "character" or "animal"
2025 Song et al.) 2025b) of- " Considering this limitation, we develop a pipeline to automatically generate rich, descriptive captions. Directly generating de- tailed descriptions from skeleton renderings is challenging, as the skeleton's 's structure are often too abstract for accurate interpretation. As illustrated in Fig. 2 the skeleton of mushroom can appear as a simple stick-like structure, making its identity unrecognizable. To address this, we first render orthogonal projections of each 3D model using pyrender Blender (Blender 2025). displaying both the mesh and its corresponding skeleton together. This approach provides the necessary visual context to make the object's 's identity and pose clearly discemible. We then leverage powerful Vi-
4

---

Published as a conference paper at ICLR 2026
sion Language Model (VLM), such GPT-4 (Achiam et al. 2023) or Gemini (Team et al. 2023), 3) to automatically generate descriptive captions. We feed the rendered orthogonal views into the VLM with specific prompt, instructing it to describe the object's identity and pose in detail.
TextuRig Curation. To generate high-quality meshes from skelctons, 3D models must have both consistent rigging and texture data. These features not consistently available in the large-scale Objaverse-XL. (Deitke et al., 2023) dataset, which is dominated by static assets. To build a suitable dataset, we develop targeted curation pipeline. Our process begins with the subset of Objaverse- XL filtered by UniRig (Zhang et al. 2025), which provides a collection of models already equipped with skeletons. However, since their processed data are often deficient in corresponding textures, we re-process the original raw assets to preserve the material information. Building upon this foun- dation, we introduce an additional filtering step to further isolate models that also have high-quality texture information by inspecting for the presence of mesh vertex colors or a texture map. This en- sures that our final dataset, TextuRig, contains assets suitable for training a texture-aware, skeleton- driven mesh generation model. To complete the dataset, we employ similar VLM-based captioning method described previously to generate detailed descriptions of each model's category and pose, creating rich, multi modai resource for research.
Canvas Interface and Data Recording. Our ctoke-recodding pipeline utilizes a specialized canvas tool. deliberately designed to mirror the standard representation of skeletons in professional 3D soft- ware (e.g., Blender (Blender 2025)). In these 3D environments, a skeleton is fundamentally defined not as sketch, but as a set of discrete joints (points) connected by rigid bones (line segments). To strictly align with this paradigm, our tool guides users to interact by clicking to instantiate 2D joints and creating connected strokes. This process naturally enforces the creation of a structured graph G2D = (Jxy: E). where the user-placed points directly correspond to the joints J xy and the connecting lines explicitly define the topological edges E that are shared by both the 2D input and the target 3D skeleton. By ensuring the input format is topologically isomorphic to the target 3D representation, we effectively bridge the domain gap between 2D user guidance and 3D generation.
3.2 SKELETAL GRAPHI VARIATIONAL AUTOENCODER
Learning Structural Embeddings. We represent 3D skeleton as an undirected graph G = (X. E), where X IR. Nx3 represents the 3D coordinates of the N joints, and E the set of edges defin- ing the skeleton's topology. To enhance the structural information in each node, we embed its 3D coordinates into a high-dimensional space, transforming the isolated positional data into a dense representation that captures local context. Our VAE encoder allows features to be aggregated and updated between neighboring nodes, enabling the latent embedding for each joint to implicitly contain structural information. The architecture is composed of GCN (Kipf 2016) and Transformer- Conv (Shi et al. 2020) for graph data structure.
Training Objective. Given input skeleton graph G = (X, E), the encoder network & maps the graph to a latent distribution, from which we sample a representation 2 = Σ(X, E). The decoder network D then reconstructs the skeleton's joint coordinates from this latent code, conditioned on the graph topology E (which is derived from the imput during training or user-drawn strokes during inference). yielding X- D(z, E). Inspired by T2I (Rombach et al. 2022), we regularize the latent space using slight KL divergence penalty to avoid arbitrarily high-variance latent spaces. This term encourages the learned posterior distribution to approximate standard Gaussian distribution NO /). The model trained by minimizing a combined objective function, which includes this KL--divergence term and an T22 reconstruction loss between the input X and the reconstructed output X. The complete training objective is provided in the Appendix.
3.3 SKELETAL GRAPH DIFFUSION TRANSFORMER
Model Architecture. For latent generation, we adopt an architecture that is based on the design of DiT (Peebles & Xie 2023), inspired by pioneering works (Labs) 2024 Wan et al. 2025) in genera- tive modeling. We replace the standard which is more suitable for graph-structure data as it ensures the attention mechanism operates only between nodes connected by an edge. To achieve semantic guidance, we first encode the text prompt using a CLIP (Radford et al. 2021) encoder. Then, within each layer of our Sk-DiT, every node em-
attentio layer with TransformerCon Shh et al. 2020),
5

---

Published as a conference paper at ICLR 2026
bedding performs cross-attention (Vaswani al. 2017) with the resulting text embedding, allowing the generation to be guided by the user's 's description.
Structural Guidance via 2D Strokes. Directly generating 3D skeletons from text presents sig- nificant challenge, as different categories of subjects often possess varying numbers of joints and distinct topologies. To overcome this structural ambiguity, drawn strokes to provide explicit structural guidance for the generation process. Our framework allows user draw desired skeleton structure using a simple provided canvas tool. As the user draws, the canvas tool records the 2D coordinates of each joint and the connections between them, effectively defining complete graph topology for the target skeleton. To train a model that can leverage this guidance, we simulate the stroke data by creating 2D projections of the ground-truth 3D skeletons, which are then perturbed to better reflect the nuances of hand-drawn input. During the generation process, we map these 2D coordinates into feature space and concatenate them with the noisy latent embedding. By conditioning the generation in this manner, the model learns to produce 3D skeletons whose structure and pose closely conform to the shape of the 2D strokes provided. This ensures that the final output aligns precisely with the user's intent.
propose method that uses user-
The generation process can be formulated learning a denoiser c that predicts the noise from the noisy latent embedding Z₁ at timestep t, conditioned on the joint XY-coordinates embeddings Ja Jay. edge topology E, and the text embedding Crext- To effectively leverage the textual condition, we incorporate classifier guidance (CFG) (Ho & Salimans, 2022) during training. The final objective function is as follows:
LSk-DIT Ezu,te E,eus
E,e. ||Ea(Ze, L, Jay E, Crex)) E =112]
3.4 ENHANCED MESH SYNTHESIS VIA TEXTURIG AND SKA-DPO
Data augmentation with TextuRig. The original SKDream model (Xu et al. 2025) utilizes Mean Curvature Flow (MCF) (Tagliasacchi et al. 2012) to generate curve skeletons from meshes, which subsequently used to fine-tune MVDream (Shi et al. 2023) for skeleton-conditioned multi-view generation. However, the efficacy of this method is constrained by low-quality training data. While existing large-scale rigging datasets (Liu al.) 2025 Song al. 2025b) provide high-quality skele- tal structures, they are often deficient in corresponding textures. Considering these limitations, we leverage our curated TextuRig dataset to augment the training data. Specifically, we utilize the rich textual descriptions generated for TextuRig as prompts, injecting them as semantic conditions into the model consistent with the MVDream paradigm. We then apply Supervised Fine- Tann-Tunnng (SFT) stage to MVDream following SKDream, thereby achieving superior generation.
Skeleton-mesh alignment guided optimization. To further enhance the structural coherence be- tween the generated mesh and its conditioning skeleton, we introduce preference-guided opti- mization strategy. This process begins by employing a reference model, Pref, to generate a pair of multi-view candidates for given skeleton and text prompt c. Subsequently, each candidate is eval- uated using a fine- tuned Dinov2 (Oquab et al. 2023) model from SKDream (Xu et al., (202)). which yields SKA Score quantifying the geometric correspondence between the mesh and the skeleton.
Through a comparative analysis of the SKA Scores, we designate the higher-scoring sample as the winner z0 and the other the loser glose thereby constructing preference dataset D= D(((c,s s, win, ). This dataset is then leveraged to fine-tune a new model, Pa, with the ob- jective of internalizing these structural preferences. By fine-tuning on these explicit preferences, the model pa learns to produce geometries that exhibit superior skeleton-mesh alignment, consequently surpassing the generation quality of the original reference model, Pref- The training objective fol- lows DiffusionDPO /Wallace et al. 2024):
wlose)e ((0)= = -eosweenntosss log logoo--100 (-3( win 60 (x. wnin ,t)l1 - || win Ger(a) win .t)
(llelose -Eo c0 lose 1) lose (xer(
lose +)113)
where t is the timestep in the diffusion process, 3 is a weighting parameter, " and Jose denote the Gaussian noises for z-tin and z/ose respectively. Intuitively, this objective encourages the model Eg to more accurately predict the noise for winning samples (ztrin) and less accurately for losing samples (1:(0e), relative the reference model.

---

Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison of Chamfer Distance (CD) ($Section 4.3
CD scores are
calculated over three metrics (CD-J2J, CD-J2B, CD-B2B) across three categories. The lowest and second-lowest scores are shown bold and underlined, respectively.
CD.J2J
CD-J2B
CD-B2B
Method' CC Score
All
Character Animal Plant
All
Character
Animal Plant
Character
Animal Plant
RigNet [SIGGRAPH 20]
0.078
0.061
0.092
0.089
0.066
0.047
0,080
0.075
0.063
0.046
0.081
0.069
SKDream (CVPR25 highlight
0.111
0.091
0.098
0.157
0.092
0.073
0.074
0.122
0.083
0.068
0.064
0.102
MagicArti. [CVPR25]
UniRig [SIGGRAPH25]
0.063
0.055
0.076
0.085
0.051
0.044
0.060
0.061
0.041
0.034
0.049
0.046
Ours
0.048
0.039
0.053
0.040
0.039
0.031
0.043
0.027
0.034
0.028
0.036
0.021
GT
RigNet
SKDream
MagicArticulate
UniRig
Ours
Figure 4: Qualitative comparison of skeleton generation ($Section 4.3
Unlike existing rigging
methods that take 3D meshes as input, our approach utilizes 2D projections of 3D skeleton. This method produces plausible skeletons that more faithfully adhere to the ground truth.
EXPERIMENT
4.1
IMPLEMENT DETAILS
Training Protocol for Skeleton Generation. We train our model on the MagicArticulate dataset (Song et al., 2025b). To ensure high-quality structural learning, curate representative subset by filtering for models with 0-30 skeletal nodes, which covers the vast majority of effective articulated assets found in real-world applications. Both the Sk-VAE and the Sk-DiT iterations using the Adam W optimizer with a learning rate of x 10-4, For the Sk-VAF we use batch size of 64 and slight KL penalty (kl_3 x 10-5), while the Sk-DiT is trained with a batch size of 128. All experiments are conducted on a NVIDIA A100 GPU (40GB).
trained for 500k
Training Protocol for Mesh Synthesis. Due the heterogeneous data sources of the MagicArtic- ulate dataset (Song el al.) (025b) making texture retrieval difficult, we instead utilize the SKDream dataset augmented with our curated TextuRig. While the SKDream dataset contains approximately 24,000 samples, our curated TextuRig augments this with an additional 6,800 high-quality skeleton and texture annotations. Examples from SKDream and TextuRig are shown in Fig. ( Following the original SKDream training setting, we initialize the diffusion model with pre-trained MVDream (Xu (ct al. 2025) weights. We then fine-tune the model using our combined dataset, training for 9,000 steps with learning rate of 10- In the SKA-DPO stage, we sample 2,000 skeleton-caption pairs from the training set. For each pair, two multi-view candidates are generated using different random noise seeds. The SKA Score is calculated for each candidate to identify the preferred and dispreferred samples, thereby constructing our preference dataset. The model fine-tuned on this dataset for 1,000 steps with learning rate of 5 x 10

---

Published as a conference paper at ICLR 2026
Table 2: Quantitative comparison of SKA score ($Section Scores are calculated over three
classes and three subclasses of animal. The highest scores are bold and the second highest are
underlined. The rows RextuRig and +SKA-DPO indicate the integration of our curated dataset and preference optimization strategy into the SKDream baseline, respectively.
Method SSK Score 1
Mean
Meanclass
Character
Animal
Plant
Apodes
Bipeds
Wings
SDEdit [ICLR22]
72.11
67.32
67.60
80.91
53.46
79.73
83.17
73.82
SKDream (CVPR25 highlight]
80.43
74.38
78.45
91.16
53.53
94.47
85,20
88. 40
+ TextuRig
82.37
76.84
84.80
91.73
53.99
94.49
86.43
92.36
+SKA-DPO
85.57
81.12
83.56
93.35
66.75
94.48
89.75
93.64
Ours (+TexnuRig & SKA-DPO)
87.83
84.36
88.70
93.75
70.63
93.33
89.29
94.55
"Charizard in Pokemon, large dragon-like
dog, Husky, big head, large ears.
potted plant"
"A free stump, roots and trunk
woman in rod dress
Sheleton
SKOreem
SKA-DPO
TeduRi
Figure 5: Qualitative comparison of skeletel-conditioned multi-view generation ($Section 4.3) Our method produces higher-quality views that more faithfully adhere to the input skeleton. For simplicity, two of the four generated views are shown.
4.2 EXPERIMENT SETUP
Baselines. We compare the Stroke3D against several methods across two distinct tasks. First, for the skeleton generation task, we evaluate our approach against four representative baselines: RigNet (Xu al., 2020), classic learning-based method; SKDream (Xu et al. 2025), non- learning method based on Mean Curvature Flow (Tagliasacchi et al. 2012), Magic (Song et al. 2025b) and UniRig (Zhang et al. 2025), generating skeleton in autoregressive manner. The second task is skeleton-to-mesh generation. Here, we adopt the experimental settings SKDream conduct direct comparison with both SDEdit (Meng et al., 2022) and SKDream itself. It is worth noting that since Stroke3D is the first framework to utilize 2D strokes as input, we deliberately select these oftte-of-the-ar mesh-based rigging methods ensure a rigorous comparison against the strongest available benchmarks, despite the difference in input modality.
Evaluation Benchmarks. We evaluate our generated skeletons and meshes on the MagicArticulate test set and the SKDream evaluation set, respectively. For mesh evaluation, we follow the protocol from SKDream, which uses test set of 108 samples for comprehensive comparison. Further details on our data processing are provided in the Appendix.
We evaluate Stroke3D's performance using two sets of metrics. To assess skeleton quality, we adopt the Chamfer Distance (CD)-based metrics (Xu et al.) 2020). includ- ing Joint-to-Joint (CD-J2J), Joint-to-Bone (CD-J2B), and Bone-to-Bone (CD-B2B). These metrics quantify the spatial discrepancy between the predicted and ground-truth skeletons. To evaluate the fi- nal generated mesh we use the SKA score (Xu et al.) 2025). which measures the aligmment between the input skeleton and the output mesh across multiple views. Following the standard protocol, perform inference four times for each sample using different random seeds and report the averaged score account for variance.
Evaluation Metrics.
8

---

Published as a conference paper at ICLR 2026
Table 3: Ablation study of preference score margin for SKA-DPO ($Section4.3) that a margin of 0.1 provides an optimal trade-off across evaluation scores.
We observe
Margin
Mean
Meanchesx
Animals
Humans
Plants
Apodes
Bipeds
Wings
0.05
86.95
83.03
93.60
88.45
67.03
93.06
88,97
93.88
0.15
87.84
84,21
94.08
88.66
69.89
92.73
90.42
94.25
0.20
86.96
83.12
93.42
88.98
66.97
94,44
87.78
93.61
Ours (0.10)
87.83
84.36
93.75
88.70
70.63
93.33
89.29
94.55
Character
Animal
Plant
Object Categories
Other
SXDream
10.9%
3.24 Charae
25.35
34.3%
TextuRig
(a) Skeleton Annotation in mesh generation. Data in SKdream shows low quality even incomplete (the bird) skeleton with corresponding mesh.
(b) Breakdown of TextuRig categories.
Figure 6: Analysis of the dataset used in mesh generation.
4.3 COMPARISON
Skeleton Evaluation. Quantitative results are reported in Tab. Compared with RigNet and SKDream, our method achieves a substantially lower Chamfer Distance across all three met- rics, indicating more accurate joint placement and bone connectivity. While MagicArticulate and UniRig shows competitive performance in specific categories such as characters, it per- forms less consistently on animals and plants. In contrast, Stroke3D achieves balanced im- provements across all categories and maintains the lowest overall error. shown in Fig. { ex- isting mesh rigging methods lack explicit struc- tural control, often leading to skeletons with re- dundant bones in some areas while missing es- sential ones in others. Stroke3D overcomes this limitation by providing direct structural control, enabling the generation of a skeleton that fully conforms the user's intent.
datapoint detapoint wo five datapcints w hg
dacazoints wo by
Loss
0.01
1k
2k
3k 5k Dk: 7kc
8kc
10k
Training Step
Figure 7: Ablation study on structural condi- tion (SSection 4.3). The model converges faster with the structural condition. The pink and blue regions highlight intervals of rapid loss decrease.
Mesh evaluation. The results in Tab 2 demonstrate the significant benefit of our proposed Tex- tuRig dataset. By augmenting the training data of the SKDream with TextuRig, we achieve superior skeleton-mesh alignment capabilities compared to the baseline. Notably, our enhanced model sur- passes the original SKDream with 1.9 improvement in Mean and 2.4 increase in Mean score, where these metrics represent the average score per instance and per class, respectively. These results confirm that our TextuRig effectively addresses the data quality limitations of prior work.
As evidenced by the results in Tab. 2 the application of DPO brings significant boost to our model's 's performance. We observe considerable rise in the primary alignment metrics, with the Mean/ and MeanClass scores reaching 87.84 and 84.21. Furthermore, our TextuRig and SKA- DPO methods result in more stable mesh generation and exhibit greater fidelity to the conditioning skeleton. As shown in Fig. 5] the vanilla SKDream produces severe geometric artifacts for the tree stump and woman, whereas our method generates a smooth and stable mesh that accurately conforms to the input pose. As demonstrated in Fig. when subjecting the generated skeleton- mesh pairs to automatic skinning and animation, our meshes maintain robust structural integrity without collapsing, proving their suitability for downstream dynamic applications.

---

Published as a conference paper at ICLR 2026
"A bird flying. "
dinosaur is hunting.
man is running."
"The free tiled by the wind."
Figure 8: Qualitative demonstration of animation stability after rigging, Our method preserves consistent motion dynamics after binding meshes to the skeleton by auto-skinning tools.
4.4 ABLATION STUDY
Ablation study on the impact of the struc- tural condition. Specifically, we train the model from scratch on small dataset of either one or five samples, with the goal of forcing overfitting and observing the convergence rate. We compare the performance of models trained with and without this structural condition (de- noted as Jæyy. As shown in the Fig. 7 the results clearly indicate that the absence of the structural condition leads to a markedly slower convergence rate.
Chanier Distance vs. Dropped Joints Number
juit: Sr Lufarr poit
16
Figure 9: Sensitivity curve with dropped joints.
In both the one-datapoint and five-datapoint ex- periments, the models trained with the Jzy condition (pink and cyan lines) consistently achieve lower loss far more quickly than their counterparts trained without it (grey and blue lines). This finding underscores that the structural prior is crucial for efficient model learning. This observa- tion extends to large-scale training, where we find that the model struggles to converge without this structural condition.
Ablation study of preference score margin. We conduct an ablation study to investigate the impact of the preference score margin in SKA-DPO on model performance. This margin a key hyperpa- rameter that calibrates the desired separation between the scores of chosen and rejected responses. As shown in Tab. B a margin of 0.10 achieves an optimal trade-off across the various evaluation scores. Therefore, adopt margin of 0.10 the final setting for our experiments.
Ablation study on skeleton generation robustness. We further investigate the sensitivity of our model to input sparsity. As illustrated in Fig. 9 the model maintains high stability and low CD scores when a small number joints are dropped in skeleton generation. This suggests that our approach is robust to minor occlusions or incomplete user sketches during the inference phase.
CONCLUSION
In this work, we present Stroke3D, novel framework for directly generating ready-to-animate 3D assets from intuitive user inputs: 2D strokes and text prompts. Stroke3D introduces a pioneering two-stage pipeline that separates controllable skeleton generation from mesh synthesis, achieving new level of structural and semantic control. For skeleton generation, our Sk- VAE and Sk-DiT mod- els convert user inputs into high-quality 3D skeletons with precise structural accuracy. Subsequently, enhanced mesh synthesis, utilizing TextuRig and SKA-DPO, produces detailed, well-aligned geometry and textures. By leveraging this decoupled approach, our method enables the creation of complex rigged assets with high fidelity to the user's vision. Experimental results demonstrate that Stroke3D outperforms existing methods, facilitating a streamlined and intuitive workflow for a wide range of animation applications.
Acknowledgements. This work was supported by the "Pioneer" and "Leading Goose" R&D Pro- gram of Zhejiang (No. 2024C01161),
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Iige Akkaya, Florencia Leoni Ale- man, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, al. Gpt-4 technical report. arXiv preprint arXiv:2303, 087774 2023.
Oscar Kin-Chung Au, Chiew-Lan Tai, Hung- Kuo Chu, Daniel Cohen-Or, and Tong- Yee Lec. Skele- ton extraction by mesh contraction. ACM transactions on graphics (TOG), 27())1110, 2008.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, al. Training helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 2022a.
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harm- lessness from ai feedback. arXiv preprint arXiv:2212.08073. 2022b.
Ilya Baran and Jovan Popović. Automatic rigging and animation of 3d characters. ACM Transactions on graphies (TOG), 26(3):72-ss, 2007.
Blender. Blender open-source create software, 2025. URL https::/wwwblendeee org
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al. Objaverse-xl: A universe of 10m+ 3d objects. Advances in Neural Information Processing Systems, 36:35799-35813, 2023.
Yufan Deng, Yuhao Zhang, Chen Geng, Shangzhe Wu, and Jiajun Wu. Anymate: A dataset and baselines for learning 3d object rigging. In Proceedings of the Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers, pp. 1-10, 2025.
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, and Kimin Lee. Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models. Advances Neural Information Processing Systems, 36:79858-79885, 2023.
Jingfeng Guo, Jian Liu, Jinnan Chen, Shiwei Mao, Changrong Hu, Puhua Jiang, Junlin Yu, Jing Xu, Qi Liu, Lixin Xu, al. Auto-connect: Connectivity- ppreservig rigformer with direct preference optimization. arXiv preprint arXiv:2506.11438 2025.
Jonathan Ho and Tim Salimans. arXiv:2207. 12598, 2022.
Classifier-free diffusion guidance.
arXiv preprint
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851 2020.
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu. Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. Lrm: Large reconstruction model for single image to 3d. arXiv preprint SxXv:23111.0400) 2023.
Diederik P Kingma and Max Welling. 1rXiv:1312.6114, 2013.
Auto auto-encoding variational bayes.
arXiv preprint
Thomas N Kipf and Max Welling. arXiv:1611.07308. 2016.
Variational graph auto-encoders.
arXiv preprint
TN Kipf. Semi-supervised classification with graph convolutional networks.
arXiv preprint
17X1v:1609.02907, 2016.
Black Forest Labs. Flux. https://git http:///ggthhbbboo com/b ack http:///tccc-ooooo----e-o--
2024.
Zeqiang Lai, Yunfei Zhao, Haolin Liu, Zibo Zhao. Qingxiang Lin, Huiwen Shi, Xianghui Yang, Mingxin Yang, Shuhui Yang, Yifei Feng, et al. Hunyuan3d 2.5: Towards high-fidelity 3d assets generation with ultimate details. arXiv preprint arXiv:2506. 16504, 2025.

---

Published as a conference paper at ICLR 2026
Harrison Lee. Samrat Phatale, Hassan Mansoor, Thomas Mesnard, Johan Ferret, Kellie Lu, Colton Bishop, Ethan Hall, Victor Carbune, Abhinav Rastogi, et al. Rlaif vs. rlhf: Scaling reinforeement learning from human feedback with ai feedback. arXiv preprint arXiv:2309.00267 2023.
Hengjia Li, Lifan Jiang, Xi Xiao, Tianyang Wang, Hongwei Yi, Boxi Wu, and Deng Cai. Magicid: Hybrid preference optimization for id-consistent and dynamic-preserve video customization. arXiv preprint urXiv:2503. 12689, 2025a.
Mingwei Li, Hehe Fan, and Yi Yang. Transnormal: Dense visual semantics for diffusion-based transparent object normal estimation. arXiv preprint arXiv:2602.0083,, 2026.
Peizhuo Li, Kfir Aberman, Rana Hanocka, Libin Liu, Olga Sorkine-Hornung, and Baoquan Chen. Learning skeletal articulations with neural blend shapes. ACM Transactions on Graphics (TOG), 40(4):1-15,2) 2021.
Ruining Li, Chuanxia Zheng, Christian Rupprecht, and Andrea Vedaldi. Dso: Aligning 3d genera- tors with simulation feedback for physical soundness. arXiv preprint arXiv:2503 22677777.7 2025b.
Sixu Li, Chaojian Li, Wenbo Zhu, Boyang Yu, Yang Zhao, Cheng Wan, Haoran You, Huihong Shi, and Yingyan Lin. Instant-3d: Instant neural radiance field training towards on-device ar/vr 3d reconstruction. In Proceedings of the 50th Annual International Symposium on Computer Architecture, pp. 1-13, 2023a.
Weiyu Li, Rui Chen, Xuelin Chen, and Ping Tan. Sweetdreamer: Aligning geometric priors in 2d diffusion for consistent text-to-3d. arXiv preprint arXiv:2310.02596 2023b.
Isabella Liu, Zhan Xu, Wang Yifan, Hao Tan, Zexiang Xu, Xiaolong Wang, Hao Su, and Zifan Shi. Riganything: Template-free autoregressive rigging for diverse 3d assets. ACM Transactions on Graphics (TOG), 44(4): 2025.
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. In Interna- tional Conference on Learning Representations, 2022.
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Mare Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.071900 2023.
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference computer vision, pp. 4195-4205, 2023.
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint 2209 /4988, 2022.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference machine learning, pp- 8748-8763. PmLR, 2021.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 014-53728-57441. 2023.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Yichun Shi, Peng Wang, Jianglong Ye, Mai Long. Kejic Li, and Xiao Yang. Mvdream: Multi-view diffusion for 3d generation. arXiv preprint arXiv:2308. /65 2023.
12

---

Published as a conference paper at ICLR 2026
Yunsheng Shi, Zhengjie Huang, Shikun Feng, Hui Zhong, Wenjin Wang, and Yu Sun. Masked label prediction: Unified message passing model for semi-supervised classification. arXiv preprint S0MV::2000.03509, 2020.
Chaoyue Song, Xiu Li, Fan Yang, Zhongcong Xu, Jiacheng Wei, Fayao Liu, Jiashi Feng, Gu- osheng Lin, and Jianfeng Zhang. Puppeteer: Rig and animate your 3d models. arXiv preprint arXiv:2508. 10898, 2025a.
Chaoyue Song, Jianfeng Zhang, Xiu Li, Fan Yang. Yiwen Chen, Zhongcong Xu, Jun Hao Liew, Xiaoyang Guo. Fayao Liu, Jiashi Feng, et al. Magicarticulate: Make your 3d models articulation- ready. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 15998- 16007, 2025b.
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Interna- tional Conference on Learning Representations, 2021. URL httpss///openteveeev.oo net/
Yang Song, Jascha Sohl-Dickstein, Diederik Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 2020.
Zhiqing Sun, Sheng Shen, Shengeao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Yan Gui, Yu-Xiong Wang, Yiming Yang, et al. Aligning large multimodal models with factually augmented rlhf. arXiv preprint arXiv:2309. 14525, 2023.
Andrea Tagliasacchi, Ibraheem Alhashim, Matt Olson, and Hao Zhang. Mean curvature skeletons. In Computer Graphics Forum, volume 31, pp. 1735-1744 Wiley Online Library, 2012.
Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiss Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint 1rXiv:2312.//805 2023.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural informa- tion processing systems, 30, 2017.
Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik. Diffusion model alignment using direct preference optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattem Recognition, pp. $228-8238, 2024.
Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314. 2025.
Tao Wu, Xuewei Li, Zhongang Qi, Di Hu, Xintao Wang, Ying Shan, and Xi Li. Spherediffusion: Spherical geometry- distortion resilient diffusion model. In Proceedings of the AAAI Con- ference on Artificial Intelligence, volume 38, pp. 6126-6134, 2024a.
Tao Wu, Yong Zhang, Xiaodong Cun, Zhongang Qi, Junfu Pu, Huanzhang Dou, Guangcong Zheng, Ying Shan, and Xi Li. Videomaker: Zero-shot customized video generation with the inherent force of video diffusion models. arXiv preprint arXiv:2412.19645 2024b.
Tao Wu, Yibo Jiang, Yehao Lu, Zhizhong Wang, Zeyi Huang, Zequn Qin, and Xi Li. Multicrafter: High-fidelity multi-subject generation via disentangled attention and identity-awar preference alignment. arXiv preprint arXiv:2509.21955. 2025a.
Tao Wu, Yong Zhang, Xintao Wang, Xianpan Zhou, Guangcong Zheng, Zhongang Qi. Ying Shan, and Xi Li. Customerafter: Customized video generation with preserving motion and concept com- position abilities. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 8469-8477, 2025b.
13

---

Published as a conference paper at ICLR 2026
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang. Dong Chen, Xin Tong, and Jiaolong Yang. Structured 3d latents for scalable and versatile 3d generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 21469-21480, 2025.
Desai Xie, Jiahao Li, Hao Tan, Xin Sun, Zhixin Shu, Yi Zhou, Sai Bi, Sören Pirk, and Arie E Kaufman. Carve3d: Improving multi- reconstruction consistency for diffusion models with rl finetuning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.
2024.
Jiale Xu, Weihao Cheng, Yiming Gao, Xintao Wang, Shenghua Gao, and Ying Shan. Instantmesh: Efficient 3d mesh generation from single image with sparse-view large reconstruction models. arXiv preprint arXiv:2404.07199 2024.
Yuanyou Xu, Zongxin Yang, and Yi Yang. Skdream: Controllable multi-view and 3d generation with arbitrary skeletons. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 314-325, 2025.
Zhan Xu, Yang Zhou, Evangelos Kalogerakis, and Karan Singh. Predicting animation skeletons for 3d articulated models via volumetric nets. In 2019 international conference on 3D vision (3DV), pp. 298-307. IEEE, 2019.
Zhan Xu, Yang Zhou, Evangelos Kalogerakis, Chris Landreth, and Karan Singh. Rignet: Neural rigging for articulated characters. arXiv preprint arXiv:2005.00533 2020.
Junliang Ye, Fangfu Liu, Qixiu Li, Zhengyi Wang, Yikai Wang, Xinzhou Wang, Yueqi Duan, and Jun Zhu. Dreamreward: Text-to-3d generation with human preference. In European Conference on Computer Vision, pp. 259-276. Springer, 2024.
Tianyu Yu, Yuan Yao, Haoye Zhang, Taiwen He, Yifeng Han, Ganqu Cui, Jinyi Hu, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun, et al. Rlhf-v: Towards trustworthy milms via behavior alignment from fine-graines correctional human feedback. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 13807-13816, 2024a.
Tianyu Yu, Haoye Zhang, Yuan Yao, Yunkai Dang, Da Chen, Xiaoman Lu, Ganqu Cui, Taiwen He, Zhiyuan Liu, Tat-Seng Chua, al. Rlaif-v: Aligning milms through open-source ai feedback for super gpt-4v trustworthiness. arXiv e-prints, pp. arXiv-2405, 2024b.
Jia- Jia-Peng Zhang, Cheng-Feng Pu, Hao Guo, Yan-Pei Cao, and Shi-Min Hu, One model to rig them all: Diverse skeleton rigging with unirig. ACM Transactions on Graphics (TOG), 44(4):
1-18, 2025.
Longwen Zhang, Ziyu Wang, Qixuan Zhang, Qiwei Qiu, Anqi Pang, Haoran Jiang, Wei Yang, Lan Xu, and Jingyi Yu. Clay: A controllable large-scale generative model for creating high-quality 3d assets. ACM Transactions Graphics (TOG), 43(4):1-20. 2024.
Ruisi Zhao, Mingming Li, Zheng Yang, Binbin Lin, Xiaohui Zhong, Xiaobo Ren, Deng Cai, and Boxi Wu. Towards finennrained hboe with rendered orientation set and laplace smoothing. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 7505-7513, 2024.
Ruisi Zhao, Zechuan Zhang, Zongxin Yang, and Yi Yang. object manipulation single image using generative models. arXiv preprint arXiv:2501. MXiv:2501..2933 12935, 2025.
Zhenglin Zhou, Xiaobo Xia, Fan Ma, Hehe Fan, Yi Yang, and Tat-Seng Chua. Dreamdpo: Aligning text-to-3d generation with human preferences via direct preference optimization. arXiv preprint arXiv:2502.04377 2025.
14

---

Published as a conference paper at ICLR 2026
APPENDICES
A DATASET PROCESSING AND ALIGNMENT
This appendix describes the dataset preparation and alignment steps used in our work. Since our framework generates rigged skeletons from 2D strokes and text. it is important to ensure that the dataset we train on is consistent in orientation and structure.
A.1 DATASET SELECTION
We use Articulation-XT. 2.0 (Song al. 2025b), which contains about 48K rigged 3D models. To ensure training stability and focus on common subjects, we select the categories character, anthro- pomorphic, animal, and plant, as they typically contain articulated structures suitable for our task. We further filter this subset to include models with skeleton node count between 0 and 30, which represents the most common range the dataset. Given that our method performs croke-to-skelette generation, which differs from prior mesh-rigging approaches, we apply the same filtering criteria used our training pipeline to the MagicArticulate test set. This ensures consistency and focuses our evaluation on common object categories.
A.2 MOTIVATION FOR ALIGNMENT
Although Articulation-XL 2.0 provides a large set rigged models, their canonical orientations are highly inconsistent. This heterogeneity poses significant challenges for stable training in skele- ton generation, as the model may learn with arbitrary orientations instead of learning semantically meaningful skeleton structures. To address this, we enforce consistent canonical alignment:
Facing + direction (forward axis).
Head pointing towards +Y direction (up axis).
Left pointing towards + direction (right axis).
which implies that the Xl-plane corresponds to the front view, the XZ-plane the top view, and the YZ-plane to the side view.
A.3 ALIGNMENT STRATEGIES
Joint-name based method.
For human-like categories such as character and anthropomorphic, many skeleton data have joint- level annotations, such 'head" and 'neck'. When these names contain enough information, search for keywords such as head, neck, or spine to identify the up-down direction, pairs of joints such left hand and right hand to identify the left-right direction, and joints such identify the forward direction. If all three can be confirmed, we directly construct the alignment.
foot to
Structural based method. joint names are missing or incomplete, we rely on the geometric structure of the skeleton, especially the symmetry of human legs. We search for pairs of bones that nearly symmetric with similar lengths and small angular difference. The longest symmetric pair is treated as the legs, and their direction defines the vertical axis. We then check which axis best preserves symmetry when joints are mirrored, and take this as the right axis. The remaining axis is assigned as the forward direction. Finally, if the resulting rotation matrix is not right-handed, we flip the right axis to correct
Principal-directin method. For categories like animal and plant, the above two methods often fail because their poses are diverse and less standardized. In this case, we compute main directions directly from the joint positions. We first determine the symmetry axis as before, then estimate the main vertical axis using different priors. For human-like models we apply PCA, assuming the body is tall and narrow. For plants, assume the dataset root joint is the physical root and use the average bone direction. For animals, we also use the root joint and average bone direction. The remaining axis is set as the forward direction, and we again flip the right axis if necessary.
15

---

robust association Published as a conference paper at ICLR 2026
Table 4: Ablation study on training data in skeleton generation. CD scores are calculated over three metrics (CD-J2J, CD-12B, CD-B2B) across five categories. The lowest and second-lowest scores are shown in bold and underlined, respectively.
CD Score
CD-J2J
CD-12B
CD-B2B
AlI
Autbro
Character
Animal Plant
Al
Anthro.
Character
Animal Plant
All
Authro.
Characler
Animal
Plant
Small
0.0544
030556
0.0361
00454
0.8469
0.0378
0.0400
RAw
1.0493
0.598
0.442
0.036
0.9477
0.9413
0.0677
0.4419
0.9261
0.0146
Baiton
0.319
0J1348
0,0212
NoDif-NoTag
0.0624
0.0334
04430
0.0887
0.0474
0.431
0.0442
0.0357
04480
0.316
0.0374
0.086
0.0324
0.0399
0.0259
VLM-assisted method. For more complex cases, such as animals in unusual poses where heuristics fail, employ vision-language model for orientation alignment. We render three orthographic views (XY, YZ, XZ) of the model and then query the VLM (Gemini 2.5 Flash (Team et al. 2023 identify which view corresponds to the front, side, and top. Based on the VLM's response, we determine the up, right, and forward axes to construct the rotation matrix. While this method is slower, offers greater flexibility.
A.4 PREPROCESSING FOR ROBUSTNESS
To make the above methods more reliable, apply several preprocessing steps. We first choose the root joint as the one closest on average to all others. Then we split the skeleton into bone segments and assign directions based distance from the root. Extremely long bones, such tails, down- weighted so they do not bias the alignment. Finally, we simplify the topology: many bones share the same starting joint (for example hands), average them one representative segment.
A.S EFFECTIVENESS OF ALIGNMENT METHODS
The joint-name based method is the most robust, and when sufficient joint names exist it almost guarantees a 100% success rate. The structural based method can correctly recognize most legs, though it may occasionally confuse the front and back, which causes the model to face backward. The principal-direction method works well for most human-like and plant models, but its perfor- mance animals is poor, since different animal categories do not share common main axis (for example, penguins are upright while crocodiles lie flat). The VLM-assisted method able to align most animals, but since render three views without texture, there remain small portion of ani- mals for which the model cannot judge the views correctly.
In practice, apply the first three heuristic methods character, anthropomorphic, and plant models, while we rely on the VLM-assisted method for animal models.
B SKELETON GENERATION ANALYSIS
In this section, we analyze how training strategies, tagging design, and dataset limitations affect our Skeleton Graph Diffusion (SkDiff) model's performance. We further discuss its generalization abil- ity to unseen concepts and complex sketches, as well as the weaknesses that arise from insufficient data coverage.
B. TRAINING DATA AND TAGGING STRATEGY
To enhance the semantic understanding of the model, we introduced descriptive tags (e.g., T-pose, symmetry) and viewpoint tags (e.g., front view, side view, top view) during training. Tags were stochastically sampled in the prompt construction process, where one or multiple descriptive tags and one viewpoint tag were optionally appended to the textual input. During validation, all available tags consistently included. This design encourages the model to learn between textual semantics and structural skeleton representation.
As described above, another training strategy adopt is rotational alignment of the dataset. The motivation is to enhance semantic consistency across training data, thereby reducing unnecessary geometric variance and facilitating model understanding during training
In summary, we conduct a comparison across four training settings: StDiff-Small. trained on a reduced subset of train dataset; SkDiff-Raw, trained the full dataset without rotational alignment;
16

---

Published as a conference paper at ICLR 2026
GT-Sholeion Text
[c]
"A Fox ptands
wilkinng wareter
atanding with
GT-Skeleton Toxt
harner the
+
Bad
"A spread
Good
Projected
Gonarated Skeletans
Good
(b)
Good
Bad
+
i. standing stsll,"
Good
Good
GT-Skeleton
Projected
Generaled
Text
Projected 2D strokes
Generated Skeletons
Figure 10: Qualitative analysis of skeleton generation. (a) shows the effect of different textual captions on inference results. (b) shows the influence of varying viewpoints. (c) shows the model's ability generalize across viewpoints and orientations for common categories.
SkDiff-NoTag, trained without descriptive viewpoint tags; and SkDiff-Full, trained the complete dataset with both tagging and alignment. The lowest and second- lowest scores in each column are highlighted in bold and underlined, respectively. The quantitative results are presented in Tab
We observe that the addition of tags, rotational alignment, and the size of the training data all have noticeable influence on model performance. Among these factors, the training data size has the most significant impact, with smaller datasets leading to clear degradation. This finding suggests that scaling up the dataset would be the most effective way further improve skeleton generation quality.
B.2 GENERALIZATION AND LIMITATIONS
Despite the improvements from tagging and alignment, the model exhibits limitations that are pri- marily attributable to the scarcity of training data. When confronted with concepts that rare or absent in the dataset (e.g., samurai or turtle), the model often generates unstable or inconsis- tent skeletons. The outputs sensitive the phrasing of the caption, though carefully designed prompts can mitigate these issues and guide the model towards more reasonable results, as illustrated case (a) of Fig. 10
The quality of skeleton generation is also strongly dependent on the information available in the 2D strokes. When strokes provide sufficient structural cues, the model can reconstruct plausible skeletons. Taking Fig. I0 (b) as example, top-view sketches of insects reveal most joints, en- abling reasonable skeleton inference. In contrast, side-view sketches often suffer from overlapping joints, leading to weak constraints and degraded generation. This highlights the challenge of stroke ambiguity cases where the model lacks strong priors.
For concepts that are well represented in the training data, such as humans and common animals, the model demonstrates good generalization. is able to generate valid skeletons under novel poses, handle mild noise in sketches (e.g., correcting small rotations), and even interpret inverted inputs by producing logically consistent skeletons, as illustrated in Fig. 10 (c), where the model correctly generates standing posture for fox even when the input sketch is vertically flipped. These observa- tions suggest that the model learns transferable priors for frequent categories, though its performance deteriorates quickly for rare underrepresented concepts.
17

---

Published as a conference paper at ICLR 2026
B.3 DISCUSSION
Overall, the analysis indicates that the primary bottleneck lies in the dataset. The use of descriptive and viewpoint tags, together with rotational alignment, contributes to better learning of semantic and geometric consistency, but these factors cannot compensate for the limited scale and coverage of the data. Enlarging the dataset and balancing the frequency of rare categories remain the most effective avenues for further improvement. We note the very recent release of Puppeteer Song et al. (2025a), which significantly expands the Articulation-XX dataset utilized this work. our future research, we plan to leverage this larger-scale data to mitigate the current data bottleneck. By integrating these expanded resources, we aim to extend Stroke3D into general-purpose text-to-articulated- asset generation pipeline, capable of handling even broader spectrum of object categories and complex topological structures beyond the current scope.
C TRAINING OBJECTIVE OF SK-VAE
We represent a 3D skeleton as an undirected graph == (X, E), where E R represents the 3D coordinates of the N joints, and E is the set of edges defining the skeleton's topology. The training process is defined by the standard VAE objective. The encoder first maps the input graph latent distribution, from which latent vector is sampled. The decoder then reconstructs the coordinates X' from z, conditioned on the original graph topology E. The model is optimized using the following loss function:
μc. a = (X, E),
== C where IN,,I), X = =Decodder(2 E)
LG-VAE = Lreeon + B LKL
where frecom the MSE loss between the original coordinates X and the decoded coordinates X', and LKL is the KL divergence between the latent embedding and standard Gaussian distribution.
The specific formulas for these loss components are: L2 Loss (Crecon): This measures the squared Euclidean distance between the original and reconstructed joint coordinates.
Cresom
|X- - X
KL Divergence Loss (.): This acts as a regularizer, forcing the distribution of the latent space learned by the encoder to be close standard normal distribution. Assuming the latent space has dimension D:
CKL = (
Σ
(n] of -In(o?) 1)
D MORE RESULTS
To further verify the robustness of our method on diverse and complex topologies, we conducted additional quantitative evaluation three specific fine-grained categories: Mythical Creatures, Toys, and Weapons. As shown in Table 5 Stroke3D consistently outperforms all baseline methods across all metrics (CD-J2J, CD-J2B, and CD-B2B), Notably, despite Toys and Mythical Creatures being treated as subsets within our broader training categories, our model achieves exceptional preci- sion, reducing the CD-B2B error on Toys 0.029, significantly surpassing the second-best method, MagicArtieulate (0.038). This demonstrates that our topology-driven training strategy effectively generalizes to a wide spectrum of articulated structures. correctly handling the unique skeletal dis- tributions of imaginary creatures and rigid articulated objects alike.
E
ETHICS AND REPRODUCIBILITY STATEMENTS
E. ETHICS STATEMENT
The goal of this research to democratize the creation of rigged 3D assets, making animation and virtual content creation more accessible non-professional users.
18

---

Published as a conference paper at ICLR 2026
Table Quantitative comparison of Chamfer Distance (CD) (SSection CD scores are calcu-
lated over three metrics (CD-J2J, CD-J2B, CD-B2B) across five categories. The lowest and second- lowest scores are shown bold and underlined, respectively.
CD-J2J
CD-J2B
CD-B2B
Method CD Score
Mythi
Toy
Weapon
Mythi
Toy
Weapon
Mythi
Toy
Weapon
RigNet [SIGGRAPH 20]
0.091
0.094
0.094
0.080
0.081
0.084
0.079
0.080
0.083
SKDrcam (CVPR25 highlight]
0.129 0.137
0.135
0.111
0.120
0.113
0.099
0.108
0.107
MagicArti. [CVPR25]
0.067 0.055
0.059
0.055
0.044
0.051
0.046
0.038
0.054
UniRig [SIGGRAPH25]
0.070 0.075
0.078
0.059
0.063
0.067
0.049
0.051
0.063
Ours
0.062 0.042
0.056
0.052 0.035
0.043
0.044
0.029
0.037
Table 6: Quantitative comparison of stroke-skeleton alignment ($Section 4.3
We report the 2D
Chamfer Distance (CD2p) quantitatively measure the structural alignment between the projected 3D skeleton and the input 2D strokes. The lowest and second-lowen scores are shown bold and underlined, respectively.
CD-J2J
CD-J2B
CD-B2B
Method
2 2D CD Score!
All
Character Animal Plant
Character
Animal Plant
All
Character
Animal Plant
RigNer SSGGRRAPH 20]
0.078
0.061
0.092
0.089
0.066
0:047
0.080
0.075
0.065
0.046
0.081
0.069
SKDream [CVPR25 highlight]
0.111
0.091
0.098
0.157
0.092
0.073
0.074
0.122
0.083
0.068
0.064
0.102
MagicAri [CVPR25]
0.052
0.079
UniRig [SIGGRAPH25]
0.063
0.055
0.076
0.085
0.051
0.044
0.060
0.061
0.041
0.034
0.049
0.046
Ours
0.048
0.039
0.053
0.040
0.039
0.031
0.043
0.027
0.034
0.028
0.036
0.021
Perturb
Perturb
"A samurai
"A dog is
+
is standing with arms slightly open."
standing
on the ground."
New View
+
Front View
"A samurai is standing arms slightly open."
"A turtle is
Side View
swimming.
User-drawn
Language Instruction
3D Mesh Generation
User-drawn
Language Instruction
3D Mesh Generation
2D Strokes
2D Strokes
Figure 11: Qualitative evaluation of generation robustness using our Canvas Tool. We present results generated from real-world 2D strokes validate practical usability. The results demonstrate that Stroke3D is (1) robust to input noise, preserving geometric fidelity even with perturbed or jit- tery strokes: (2) view-invan wew-invariant, supporting generation from arbitrary camera angles; and (3) gener- alizable to rare concepts, successfully lifting complex, nut-of-distribution subjects (c.g.. 'Samurai', "Turtle') into rigged 3D models.
Dataset and Licensing: Our curated dataset, TextuRig, is derived from existing large-scale, publicly available datasets, including Objaverse-XX XL and the filtered subset from UniRig. We use these assets in manner consistent with their original licenses and terms of use. The skeleton generation model trained on the MagicArticulee dataset, which is intended for academic research.
Potential for Misuse and Bias: Like other generative models, Stroke3D could potentially be mis- used to create content that is misleading, biased, or harmful. This is not the intended use of our technology. We also acknowledge that the datasets used for training may contain inherent biases
19

---

Published as a conference paper at ICLR 2026
(e.g., in the distribution of object categories or human representations), which our model may learn and perpetuate. Future work could explore methods to identify and mitigate such biases.
The authors declare competing interests conflicts of interest related to this work.
E.2 REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. To facilitate this, we provide detailed descriptions of our methodology. data, and experimental setup throughout the paper and its appen- dices. We plan to release the code for Stroke3D, our pre-trained models (Sk- VAE, Sk-DiT, and the final mesh synthesis model), and the curated TextuRig dataset upon publication of this paper.
CONCLUSION
Limitation and future work. While our two-stage Stroke3D successfully generates rigged meshes from 2D strokes and text prompts, its performance is constrained by the limited pose variation in the training data. Therefore, our future work will proceed two key directions. First, we plan to enrich the dataset with more diverse skeletal poses to address the data limitation. Second, we aim to develop an end-to-end network that can produce a rigged mesh directly from text prompt.
Usage of LLM. In this work, employed Large Language Model (LLM) to polish the prose and enhance the overall quality of the text.
20


Reviews:
[{"review_id": "zSwBeKaV8l", "reviewer": "Reviewer_zQuz", "summary": "The paper presents Stroke3D, a skeleton-first pipeline that produces rigged 3D meshes from user-drawn 2D strokes plus a text prompt. The method learns a latent space for skeletal graphs with a Skeletal Graph VAE (Sk-VAE) and performs denoising in that space using a Skeletal Graph Diffusion Transformer (Sk-DiT) conditioned on text and stroke features; the decoded skeleton then conditions a mesh generator. A second stage improves skeleton-to-mesh synthesis by curating TextuRig (a textured, rigged subset with captions) and applying skeleton–mesh-alignment-guided Direct Preference Optimization (SKA-DPO). On MagicArticulate (skeletons) and SKDream (meshes), the approach lowers Chamfer distance and raises SKA scores, with qualitative results indicating better adherence to stroke structure and semantics. The work is promising but constrained by data coverage, evaluation breadth, and reliance on VLM-assisted curation.", "questions": "1. Robustness to real sketches. How does performance change with real, noisy human strokes (varying density, missing joints, occlusions)? Please provide sensitivity curves versus stroke sparsity/noise.\n2. Generalization to rare/unseen concepts. Can you quantify success rates for long-tail categories (e.g., “samurai,” “turtle”) and the effect of prompt phrasing beyond qualitative examples?\n3. Downstream animation quality. Beyond SKA/CD, do the produced meshes remain stable under standard auto-skinning and motion-retargeting tests (e.g., penetration, joint collapse) across sequences?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Quality. The technical description is detailed and concrete, including the conditioning mechanism, classifier-free guidance, and the SKA-DPO objective. Training protocols and ablations (e.g., stroke guidance aiding convergence; the DPO margin study) are appropriate. Quantitatively, Stroke3D improves CD metrics and SKA scores over strong baselines.\n2. Clarity. The end-to-end pipeline is clearly presented with informative figures (data preparation and overall architecture), and the contributions are explicitly itemized. The appendices document alignment and data-processing choices relevant for reproducibility.\n3. Significance. A stroke-and-text interface can lower the barrier to authoring rigged, animation-ready assets. The TextuRig curation and alignment-guided preference optimization are broadly useful ideas for skeleton-to-mesh generation.", "weaknesses": "1 Data dependence and coverage. Performance and generalization rely on curated sets (MagicArticulate, SKDream, and TextuRig). The paper acknowledges dataset limitations and sensitivity to rare concepts (e.g., plants before DPO). Scaling and coverage remain open issues.\n2 Stroke simulation vs. real inputs. Structural conditioning is trained on perturbed 2D projections of 3D skeletons rather than large-scale human sketches. This domain gap may reduce robustness to messy real drawings; analysis of viewpoint or stroke ambiguity is limited.\n3 Evaluation breadth. Skeleton evaluation is primarily CD-based and mesh alignment uses SKA on a 108-sample set following SKDream. There are no user studies or downstream animation stress tests (e.g., auto-skinning stability under motion), and comparisons to recent autoregressive skeleton generators under stroke constraints are sparse.\n4 Ablations and controls. While DPO-margin and stroke-conditioning ablations are provided, the incremental effects of TextuRig versus SKA-DPO are not fully disentangled across categories, and variance over random seeds is not reported.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "D3eyF8qpmQ", "reviewer": "Reviewer_X4bw", "summary": "This paper proposes a novel framework for synthesizing rigged 3D skeletons from 2D drawn strokes and text prompts. The authors first curate a dataset from existing open-source 3D rigging collections, then train skeleton VAE and diffusion models to enable generation conditioned on 2D strokes. To further enhance performance, the paper introduces a skeleton-mesh alignment process guided by an optimization step, improving the model's fidelity. Experiments demonstrate that the proposed method outperforms existing approaches.", "questions": "A potential limitation of the method appears to be its robustness to imperfect user inputs. The paper primarily showcases results with clean, well-defined 2D drafts. However, in real-world scenarios, user-drawn strokes are often noisy, shaky, or not perfectly straight. How would the model perform under such conditions? I would encourage the authors to provide an analysis or at least a qualitative study on the model's sensitivity to variations in input stroke quality.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper addresses an interesting and novel task: generating 3D articulated objects from a combination of 2D strokes and text descriptions. The problem itself is well-motivated and interesting.\n2. The work improves upon existing baselines in several areas, including dataset curation, VAE design, and a post-hoc optimization stage. This results in a stronger baseline for future research in rigged 3D generation.\n3. The paper is well-written, clearly structured, and easy to follow.", "weaknesses": "1. My main concern revolves around the paper's technical novelty. The proposed method appears to be a successful combination of existing technologies: \n* A data curation step that refines datasets from prior work (Unrig, MagicArticulate).\n* Slight modifications to the existing Sk-dream model architecture.\n*  The application of a post-training optimization phase, which is a common technique for refinement.\nConsequently, the contribution seems to lie more in effective engineering and integration than in providing new fundamental insights.\n\n2. The experimental evaluation is not sufficient. A key claim is the ability to generate rigged objects from 2D sketches, yet there is no quantitative metric to evaluate the consistency between the input 2D sketch and the generated 3D object. I suggest that the authors include such a metric (e.g., projection-based distance) to substantiate their claims.\n\n3. The process for creating the training data is not sufficiently detailed. Specifically, the paper should clarify how the paired 2D drafts and their corresponding 3D ground-truth (GT) models were generated. This information is crucial for reproducibility.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GzXhJ8xaS9", "reviewer": "Reviewer_6nvo", "summary": "The paper introduces Stroke3D, a novel framework that generates rigged 3D meshes from user-drawn 2D strokes and text prompts. Unlike prior methods that generate a 3D mesh first and then rig it, Stroke3D adopts a skeleton-first generation pipeline. Experiments on MagicArticulate and SKDream benchmarks show that Stroke3D outperforms baselines such as RigNet, UniRig, and SKDream, achieving better Chamfer Distance and SKA scores. The system demonstrates both structural control and high visual quality, enabling intuitive rigged 3D asset generation.", "questions": "1.How is the camera viewpoint determined when projecting the 3D skeleton into 2D strokes?\n\n2.What are the specific design details of the Skeletal Graph Variational Autoencoder (Sk-VAE)? According to the paper, the VAE decoder requires the set of edges to reconstruct the skeleton. So how are these edges obtained during the generation process?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. First framework enabling rigged 3D generation directly from 2D strokes and text.\n2. Outperforms baselines such as RigNet, UniRig, and SKDream, achieving better Chamfer Distance and SKA scores.\n3. Introduce TextuRig, a dataset of textured and rigged 3D meshes with captions", "weaknesses": "1. Other pipelines generate skeletons from 3D meshes, while this paper generates skeletons directly from 2D strokes. Therefore, comparing skeleton quality metrics between these methods is not entirely fair, since the input modalities differ significantly in both information richness and structural constraints.\n\n2. The contributions of this paper are relatively limited. It mainly proposes a model that generates 3D skeletons from 2D strokes and introduces a modest extension of the SKDream dataset, rather than delivering fundamentally new insights or large-scale advancements to the field.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eJ0r3kDG5L", "reviewer": "Reviewer_vd39", "summary": "In this paper, the authors propose Stroke3D, a novel framework that generates mesh-skeleton pairs from 2D strokes and text prompts. The pipeline first uses an Sk-VAE/DiT combination to achieve controllable skeleton generation, conditioned explicitly on the 2D strokes for structure. It then synthesizes the mesh by enhancing a model with the TextuRig dataset and applying the SKA-DPO preference optimization for geometric fidelity.", "questions": "1. Missing citation: \nAuto-Connect: Connectivity-Preserving RigFormer with Direct Preference Optimization, which also uses DPO for skeleton generation.\n\n2. The current placement of figures, particularly Figure 4 and Figure 5 (Experimental Results), is suboptimal as they appear within the Method section. One suggestion is to rearrange the figures to fit the paper structure.\n\n3. Verify and correct the mark used for the B2B-CD (All) metric in Table 1, as it appears to be incorrect.\n\n4. Include a brief, clear statement in the main paper that you select part of the MagicArticulate Dataset for both training and testing, rather than only mentioning this information in the Appendix.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The paper tackles an interesting and novel task: generating skeleton-mesh pairs from 2D strokes and text prompts, which has not been explored in prior work.\n\n2. The proposed method demonstrates effective results for skeleton-mesh generation. The two-stage generation pipeline effectively decouples skeleton and mesh generation, allowing each component to be optimized independently while maintaining geometric consistency through the skeleton-conditioned mesh generation stage.", "weaknesses": "1. The paper misuses the term \"rigged model.\" In standard 3D graphics terminology, rigging includes both skeleton and skinning weights. Since this work only predicts the skeleton without skinning weights, the authors should use precise terminology such as \"skeleton prediction\" to avoid misleading readers about the actual technical scope.\n\n2. I do not agree that the dataset is one of the main contributions. (1) It is curated from UniRig rather than being newly collected. (2) Since both SkDream and UniRig source from Objaverse-XL, potential duplicates should be identified and reported. (3) The authors should justify why they did not use MagicArticulate's Articulation-XL, which contains more rigged models. While Articulation-XL lacks textures, these could be obtained from the original Objaverse-XL sources.\n\n3. The method predicts only connected joint positions without their hierarchical tree structure. Prior works (RigNet, UniRig, MagicArticulate) all predict parent-child relationships between joints, which are essential for animation workflows. Without this connectivity information, the output cannot be directly used in standard 3D animation pipelines.\n\n4. The category selection from Articulation-XL (human, animal, plant) lacks justification. Plants are neither a major category in the source dataset nor commonly associated with skeletal rigging needs, unlike toys or articulated objects. The authors should provide selection statistics and explain why underrepresented categories like plants are included while more relevant categories are excluded.\n\n5. The text prompt is only used for skeleton generation, while mesh generation proceeds without textual guidance. This is counterintuitive, as mesh geometry and appearance are more naturally conditioned on text descriptions than skeletal structure.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)