Anime-Ready: Controllable 3D Anime Character Generation with Body-Aligned Component-Wise Garment Modeling
Authors: Jiachen Qian
Hongye Yang
Youtian Lin
tianhao zhao
Feihu Zhang
Yao Yao
Hengshuang Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: 3D anime character generation
stylized body modeling
component-wise garment generation
Primary area: generative models

Abstract: 3D anime character generation has become increasingly important in digital entertainment, including animation production, virtual reality, gaming, and virtual influencers. Unlike realistic human modeling, anime-style characters require exaggerated proportions, stylized surface details, and artistically consistent garments, posing unique challenges for automated 3D generation. Previous approaches for 3D anime character generation often suffer from low mesh quality and blurry textures, and they typically do not provide corresponding skeletons, limiting their usability in animation. In this work, we present a novel framework for high-quality 3D anime character generation that overcomes these limitations by combining the expressive power of the Skinned Multi-Person Linear (SMPL) model with precise garment generation. Our approach extends the Anime-SMPL model to better capture the distinct features of anime characters, enabling unified skeleton generation and blendshape-based facial expression control. This results in fully animation-ready 3D characters with expressive faces, bodies, and garments. To complement the body model, we introduce a body-aligned component-wise garments generation pipeline (including hairstyles, upper garments, lower garments, and accessories), which models garments as structured components aligned with body geometry. Furthermore, our method produces high-quality skin and facial textures, as well as detailed garment textures, enhancing the visual fidelity of the generated characters. Experimental results demonstrate that our framework significantly outperforms baseline methods in terms of mesh quality, texture clarity, and garment-body alignment, making it suitable for a wide range of applications in anime content creation and interactive media.

Published as a conference paper at ICLR 2026
ANIME-READY: CONTROLLABLE 3D ANIME CHARAC- TER GENERATION WITH BODY-ALIGNED COMPONENT-
WISE GARMENT MODELING
Jiachen Qian' Hongye Yang", Youtian Lin', Tianhao Zhao², Feihu Zhang?; Yao Yao"! Hengshuang Zhao' The University of Hong Kong, DeraamTech, NNajjing University
ABSTRACT
Automated generation of 3D anime characters has become increasingly important in digital entertainment, including animation production, virtual reality, gaming, and virtual influencers. Compared to realistic human modeling, anime-style mod- eling requires exaggerated proportions, stylized surface details, and artistically consistent garments, posing unique challenges for automated 3D generation. Exist- ing anime-style approaches often suffer from low-quality meshes, blurry textures, and lack of inner skeletons, which limits their usability in animation. In this work, we present novel framework for high-quality 3D anime character generation overcome these limitations by combining the expressive power of the Skinned Multi-Person Linear (SMPL) model with precise garment modeling. We extend the SMPL model to Anime-SMPL to better capture the distinctive features of anime characters, which enables unified skeleton generation and blendshape- based facial expression control, rendering the generated characters animation-ready. To com- plement the body model, we introduce a body-aligned component-wist garment generation pipeline, which models hairstyles, upper garments, lower garments, and accessories as structured components aligned with the underlying body geometry. Furthermore, our method produces high-quality skin and facial textures, as well detailed garment textures, enhancing the visual fidelity of the generated characters. Experimental results demonstrate that our framework significantly outperforms baseline methods in terms of mesh quality, texture clarity, and garment-bor align- ment, making well-suited for a wide range of applications anime content creation and interactive media.
Figure I: Our Anime-Ready generates high-quality, controllable 3D anime characters from text or single image, with fine-grained control over actions such as finger movements and facial expressions.
Project leader.
1 "ooresppnding author.
This research was supported by DreamTech and the TP belongs to DreamTech.

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1 INTRODUCTION
While recent advances in 3D human modeling have led to breakthroughs in reconstructing real- istic human avatars, generating high-quality, animatable 3D characters in anime style remains highly challenging task. Anime characters often feature stylized anatomy, typically exhibit more complex garments, diverse hairstyles, and exaggerated facial features, which challenges traditional reconstruction pipelines.
Recent methods (Peng et al., 2024, He al. 2024b Qian et al. 2025 Dong al. 2025 reconstruct anime characters from images using large models (Hong ct al. [023]] generating plausible geometry and textures. However, these approaches often fail to capture fine details, such as hand poses and hair structure. Besides, the results of these models are typically unrigged and lack consistent topologies, which is unsuitable for parametric animation.
contrast, parametric body models such
as SMPL (Loper et al. 2023) and SMPL-X (Pavlakos et al. 2019) are widely used for realistic and animatable human bodies (Cao et al.
2024 Hong et al. 2022: Huang et al. 2223a b: Pan et al. 22024 Wang al. 2024a b. Yang al. 2024 Zhang et al. 2022 Dong et al. 2023; Xiu et al. [002]] 2023 Huang et al. 2024a| Liao et al. (024). These models provide well-
2024: Dong et al.
defined skeletons and consistent mesh topologies for easy animation. However, shown in Figure 7
they are designed for realistic human proportions and cannot represent the exaggerated features of anime-style characters.
In this paper, we present a novel pipeline that combines the controllability of SMPL-based models with the generative power of 3D diffusion models to synthesize high-quality anime-style 3D characters that are animatable. Unlike previous methods (Peng et al. [0224] Qian et al. (025) that directly generate the entire character, our approach decomposes the character into modular components: body, hair, upper garments, lower garments, and accessories. Each part is generated as a separate high-resolution textured mesh for the final assembly.
To make the generated 3D anime characters animatable, we introduce Anime-SMPL. a parametric model designed for style characters with high-quality meshes suitable for animation. Anime- SMPL retains the core SMPL structure while incorporating modifications for exaggerated eyes and anime-style proportions. Our Anime-SMPL. also provides consistent UV layout across characters, enabling direct texture generation in UV space. Conditioned on both character image and textual description, we adopt a mieti-view diffusion model to predict UV textures for six semantic regions: body skin, facial skin, eyebrows, eyelashes, and the left and right eyes.
To generate meshes for hair, garments, and accessories, we propose a Multi-Shape Diffusion Trans- former (DiT) architecture incorporating a Mixture- of--eerss (MoE) (Jacobs et al.) 1991) module. This unified model dynamically routes inputs to specialized branches according to garment types to produce distinct meshes. We further guide the generation be consistent with the body geometry by sampling points from the Anime-SMPL surface as an explicit geometry prior. As result, our method produces well-aligned, separate meshes for hair, garments, and accessories.
In addition, we use a diffusion model to generate images of each garment component, conditioned on a reference image of the full body. Equipped with multi-component self-attention mechanism, this model can selectively focus on relevant regions in the reference image. The resulting component images are then processed individually by an MVAdapter (Huang et al.) 2024b) produce the final textures of the garments.
The experiments demonstrate that our method achieves state-of-the-art performance in 3D anime character generation. Our method significantly outperforms previous approaches in both geometry and texture quality, while maintaining the highest fidelity to the input images.
Our main contributions are summarized as follows.
We introduce -MML, a unified human body template designed specifically for anime- style 3D characters. This template not only enables high-quality, animatable body mesh generation, but also facilitates direct texture synthesis in UV space.
We propose a Multi-Shape DiT architecture incorporating a MoE mechanism that leverages character geometry as guidance. This design allows single unified model to generate distinct meshes for each garment component while ensuring mesh compatibility with the underlying body shape, significantly reducing interpenetration artifacts.
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We propose high-resolution component wise texture generation pipeline that employs diffusion model to disentangle garment components from the input image. This enables independent texture synthesis for each component while mitigating color bleeding across different regions.
Our method surpasses previous approaches anime-style 3D character generation, achieving higher fidelity and overall quality. Moreover, our approach makes the generation of anime- style 3D characters practical for real-world applications, enabling downstream tasks such as garment retargeting, motion control, and facial expression control.
RELATED WORK
2.1 3D OBJECT GENERATION
In recent years, diffusion models have made remarkable breakthroughs in image generation. However, compared to massive 2D images, the scarcity of 3D data makes it challenging to directly train highly generalizable 3D generative models. To alleviate this issue, Poole et al. (2022) introduced the Score Distillation Sampling (SDS) loss, which leverages pre-trained 2D diffusion models to guide 3D)
generation. While SDS- based approaches (Chen et al. 2023 Lin et al. 2023 Poole et al. 2022 Qian et al. 2023 Raj et al. 2023 Tsalicoglou et al. 2024 Wang et al. 2023b have significantly improved 3D generation performance, the resulting geometry often lacks fidelity, as SDS does not inherently enforce geometric consistency. Some methods (Lu al.. 2024 Long al. 2024 Wu et al., 2024a) attempted to deal with this problem by reconstructing 3D geometry from multi-view normal maps, which be inferred from single image synthesized by learned view generation, but they may suffer from limited volumetric consistency and difficulty in handling complex topologies.
With the increasing availability of 3D data, emerging trend in recent methods is to move beyond SDS-based supervision. RRdedaeed approaches(Hong et al. 2023 Wang et al. [2024c] Li et al.
2023 Xu al. 2024a by Tang al. 2024 Zhang al. 2024a b) predicted triplane representations
from limited inputs to reconstruct 3D models. Other methods (Wu et al. 2024b Cui et al. 2024) applied diffusion models directly on 3D data to generate triplanes for 3D synthesis. In addition to triplane representations (Ren et al. 2024 Xiang et al. (024)), alternative formats such as sparse voxel grids (Xiang et al. 2024) and VecSet-based representations (Zhang al. 2023 2024c Li et al. 20244]
Chen et al. 2024b Zhha et al.
2025
Li et al. 2025) are also gaining traction in 3D generation.
2.2
GARMENT GENERATION
Sewing patterns have been widely adopted in garment generation methods (He et al. 2024a Liu ct al, 2023) due to physical realism and alignment with realistic clothing construction. These representations provide clear semantic structure and high controllability, allowing for fine-grained adjustment of garment fit, style, and layout. However, generating accurate patterns requires expert knowledge, and may struggle with stylized unconventional garments. Besides, inferring sewing patterns from images or text remains a challenge due to structural and alignment constraints. In addition to pattern-based generation, Qiu et al. (2023) reconstructed detailed 3D dynamic clothing from monocular video, Sarafianos et al. (2024) leveraged Long et al. (2024) to generate garments.
Luo et al. (2025) proposed body-aligned generation method of wearable assets based on native 3D diffusion. However, post-processing is still required after generation, since there are still small penetrations between the garment and body.
2.3 CHARACTER GENERATION
Previous works have achieved significant progress in realistic 3D human body generation, Xiu et al. (2022) leveraged implicit representations from normal maps to reconstruct detailed clothed human surfaces while Xiu et al. (0023) performd explicit normal integration for high-quality geometry recovery. [Haan et al. (2023a) introduced Pixel-Semantics Difference-Sampling to optimize body and clothes, and Cao et al. (2024 introduced a mawallobevvationspaaaccee design consisting of a canonical space and posed space related by learnable deformation field for jointly optimization. Dong et al.) (2024) proposed a layer-wise generation strategy to get better clothing structure and higher realness. [iao et al. (2024) further leveraged the synergy of 2D diffusion model and parametric body model
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to generate digital characters. Several prior works have also explored 3D character generation in the anime style. Peng et al. (2024) used a large reconstruction model to generate anime-style 3D characters, but lack of details in many aspects. [He et al. (2024b) and Dong et al. (2025) adopted component-wise generation strategy to improve overall quality, and Qian et (2025) utilized sparse voxel increase the resolution of generated meshes, enhancing quality of detailed regions like face and hands. However, due to limitations in fine-graine, details, all these methods still fall short of producing truly usable 3D anime characters.
METHOD
Our pipeline, illustrated in Figure 2 first generates image of the character in canonical pose (e.g., A-pose) from either a textual description or an input image. Based on this canonical-posee image and the Anime-SMPL. template, Analogous to SMPL, we use a joint regressor to obtain joint locations. Subsequently, we sample points on the surface the estimated Anime-SMPL mesh and encode them into body latent tokens using VecSet VAE (Zhao et al., 2025). These body latent tokens, together with noised latent tokens, garment label tokens, and the canonical-pos image, are fed into our MoE-structured Multi-Shape DIT network to generate 3D garment meshes. Finally, we generate texture maps for both the body and individual garments.
estimate the Anime-SMPL, parameters of the target character.
Anime-SMPL
Agliiai
T2l model
Body Estinatio
Noised Latent Tokens
Body Testaring
Redy I Laten Tokerr
Regression
IZ1 model
Joint Estincttion
DINO
Garmeni Testuingg
Encoder
MoE stratured Multi
Bady Points Sompllig
Figure 2: Pipeline for controllable 3D character generation based on Anime- and Body- Component- Wise Garment Modeling.
3.1 2D CANONICAL POSE CHARACTER GENERATION
As shown in Figure 2 our framework generates a front-view image of character a canonical pose from either textual description or image of the character in an arbitrary pose.
Text-to-Image Synthesis. We fine-tune PixArt-E (Chen et 2024a) using paired data consisting of textual descriptions and corresponding front-view images of characters in canonical pose.
Image-to-Image Synthesis. Similar to CharacterGen (Peng et al. 2024) and StdGen (He al. 2024b), we employ Reference and CLIP encoder (Radford et al.) 2021) as image feature extractors, and incorporate pose information using a general a-pose skeleton image as an additional condition. To train our image-to-image model, we render diverse anime characters in full-body views under various camera viewpoints, poses, and facial expressions. To further improve the generalization performance of the model, apply data augmentation strategies, including varying illumination conditions, modifying contour line styles, and upper-body-fe bodysfocused augmentations commonly used in pose estimation tasks (Xiao et al. 2018 Sun al. 2019).
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3.2 ANIME-SMPL
The SMPL model (Loper et al., 2015), has been instrumental in human shape estimation and 3D reconstruction, due to its ability to capture various body shapes and poses. However, the application of SMPL to anime characters is limited, owing to substantial geometric and stylistic differences, as illustrated in Figure 7 To overeome this limitation, we introduce Anime-SMPL, a parameterized body model tailored for anime-style characters, designed to better capture their unique proportions and stylistic characteristics.
Anime-SMPL Parameterization. In contrast to SMPL (Loper et al. 2013). which parameterizes both pose and shape, our framework focuses exclusively on shape modeling, as our aim to reconstruct 3D anime characters from input images depicting fixed canonical pose. As in SMPL, we apply Principal Component Analysis (PCA) to model shape variations in anime characters. We perform PCA dataset of 20,000 characters, each represented by 12,489 vertices, and retain the top 98 principal components to capture the dominant modes of shape variation. Similarly to SMPL, estimate the joint regressor matrix J using non-negative least squares (NNLS), by solving the following optimization problem:
minimize J20
JV - Bvll s.t. J1N = 1K
where V RN3 is the vertex matrix, By Rx3 the target joint location matrix, 1y is an all-ones vector of length N, and 1 is all-ones vector of length K. The problem is solved using Splitting Conic Solver (SCS). The linear blend skinning (LBS) weights are directly adopted from pre-defined values in our dataset.
Shape Parameters Estimation. To estimate shape parameters for arbitrary anime charac- ters, train shape prediction network on front-view images of characters exhibiting di- verse clothing styles and body shapes. The network is optimized using the mean squared error (MSE) between the predicted parameters / and the ground truth B. In practice, employ ResNet-bassd architecture (He et al. 2016)
shape prediction network.
3.3 MOE-STRUCTURED
Timestep
Inpur Image
Noisy Tarent Tokans
Body Leseaalle
MULTI-SHAPE DIT FOR GARMENT GENERATION
I Tstent Tokens
Tabel
Despite recent progress in 3D generation algorithms, syn- thesizing high-quality anime characters remains challeng- ing. The difficulty arises primarily from the complex geometric structures inherent to anime characters, such as diverse hairstyles and intricate costumes. Previous approaches typically generate the entire character, mak- ing difficult precisely control fine- mna-irained details in each part. To mitigate these challenges, we propose a component-win generation strategy. Specifically, for each character, we decompose the outfit, excluding the body, into four components: hairstyles, upper garments, lower garments, and accessories. We employ the VecSet Dif- fusion Model (Zhang et al. 2023 2024c|
Embedding
DINO
MLP
El
I:
E:
E4
Linear
Modillation
DIT
Router
X24
Li et 2024
Output
Chen et al. 2024b: Zhao et al.) 2025 Li et al. 2025) as the core generative model to synthesize these components.
MoE-structured DIT Block. The original VecSet Diffu- sion Model cannot directly generate garment components from single image. To address this limitation. we design a MoE-structured Multi-Shape DiT architecture that en- ables independent generation of each garment component. Figure iillustrates the architecture of our MoE-structured Multi-Shape DIT.
Linear
Figure 3: Overview of our MoE- structured Multi-Shape DiT.
In the VecSet Diffusion Model, the input image is first encoded by DINOv2 (Oquab et al. 2023 to produce conditioning tokens for the DiT backbone network. We adopt of (MoE)
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design comprising four MLP expert branches, each dedicated to specific garment component. In our MoE-structured Multi-Shape DiT, all parameters are shared except those of the four MLP experts, enabling precise, component- generation with minimal parameter overhead. We use learnable label token to guide the network in generating the corresponding component.
Body-Aligned Garment Generation. Relying solely image-based conditioning is insufficient for generating body-aligned garment components with the VecSet Diffusion Model, as it often leads shape misalignments and noticeable interpenetration artifacts. In this section, introduce an additional geometric condition to guide the shape and spatial alignment of the garment components.
Specifically, perform point cloud sampling on the 3D body surface and encode it using the VecSet VAE encoder to obtain body latent tokens. The body latent tokens are then concatenated with the noised garment component tokens input to the VecSet Diffusion Model. To reduce computational overhead, we encode the body latent tokens at lower resolution: the garment component tokens have length of 3072, while the body latent tokens are set 512. With this conditioning, which directly encodes spatial information of the body surface, the model is able generate garment components accurately aligned with the underlying character geometry, which greatly reduces penetration issues and improves overall fidelity. The garment components are represented as Signed Distance Fields (SDFs) as output and are finally converted to 3D meshes via the marching cubes algorithm.
Canp--Cn Self-Attemann
ClOS Atentios
Cose Sn
MVAclppeer
VAR
Back Projection
Embellingg
Figure 4: Pipeline of our Component-Wise High-Resolution Texture Generation.
3.4 TEXTURE GENERATION
Previous works, such as MVAdapter (Huang et al. 2024b), have demonstrated strong performance in multiview generation. Motivated by these advances, adopt MVAdapter our texture generation pipeline. Specifically, our method divides texture generation into two stages: one for the body and the other for the individual garment components.
component in
Body Texture Generation. Leveraging the unified UV layout of Anime-SMPP across all characters, method facilitates texture generation in UV space. We employ multiview diffusion model (Huang şt all, 2024b) as the texture synthesis network, which semantically decomposes the body texture into six regions: body skin, facial skin, left eye, right eye, eyebrows and eyelashes. The diffusion model takes region-specific text prompts as input and is conditioned on the character image to generate the UV texture for each region. A representative result shown in Figure 5
Input
Body Skin
Face Skin
Left Eye
Right Eye
Eyebrow
Eyelash
Figure 5: An example of the body texture we generated.
Component-wise Garments Texture Generation. Given the meshes all garment components, initially attempt normal maps input, and employ MVAdapter (Huang al. (024b).

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conditioned the canonical-pone coooe character image, to synthesize multi-view images for each gar- ment component. However, this approach results in color bleeding, where the appearance of each component is adversely affected by neighboring regions.
To address this issuc, we design a component wise high-resolution texture generation pipeline, as illustrated in Figure Following Dong et al. (2025), first train diffusion model to decom- pose the full-body image into enlarged and independent views of individual garment components. Specifically,
the normal maps of all garment components as input and condition the model on the canonical-pose character image. A multi-component self-attention mechanism is employed to facilitate information exchange across components, and the resulting features are fused with label and timestep embeddings via cross-attention. This design enables the generation of high-quality images for these garment components. These segmented images are subsequently fed into the MVAdapter to synthesize multiview renderings, which are finally projected onto the 3D surface to obtain the complete texture map for each component.
Thanks to our component-wise garment generation strategy, self-occlusion is significantly alleviated when projecting textures from six canonical views (front, back, left, right, top, and bottom). Moreover, generating textures for each component allows for higher-resolution allocation, allowing for the generation of high-quality texture maps.
ChuracterGen
SWIGEN
(Layunnn D 2.0
Ours
Figure 6: Qualitative comparisons between our method and previous state-of-the-e ttt-hhh-rt methods
4 EXPERIMENTS
4.1 IMPLEMENT DETAILS
Training. For the Anime SMPL shape prediction network, training is conducted on single NVIDIA L20 GPU and takes approximately 4 hours. For the MoE-structured Multi-Shape DiT, we use 16 NVIDIA A100 GPUs with Adam optimization and a learning rate of 10-4, which requires about 10 days of training. For the remaining modules, 2D Canonical Pose Character Generation, Body

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Table I: User study results. The score ranges from
where 5 is the best score.
Mesh Quality t
Texture, Quality'
Methods
Fidelityt
CharacterGen (Peng et al.
2024)
2.58
2.14
2.51
StdGEN (He et al. 2024b)
2.69
2.23
2.52
Huanyuan3D 2.0 Zhao ct al.
2025
3.14
3,49
3.42
Ours
3.83
3.75
3.74
Texture Generation, and Component-wist Garment Texture Generation, each is trained independently on 8 NVIDIA A100 GPUs for approximately 2 days.
Ineference.
The image generation stage takes 5 seconds, ANIME-SMPL parameter prediction takes 2 seconds, the Mtu-Strue Multi-Shape DiT requires total of 40 seconds, body texture generation takes 10 seconds, and garment texture generation takes 360 seconds.
Dataset. All models are trained on our private dataset, which consists of 20k anime characters aligned to unified body template. Specifically, each character shares the same body mesh topology, including identical vertex count, vertex ordering, and face connectivity. For every character, garments are consistently divided into four components: hairstyle, upper garment, lower garment, and accessories. For characters missing any of these components, the corresponding part left unassigned.
4.2 COMPARISONS WITH THE METHODS
To ensure fair comparison with state-of-the-ar anime-style character generation methods, we evaluate our model using both front-view images of characters in arbitrary poses collected from the Internet, as well as additional synthesized samples with diverse poses. The qualitative results are presented in Figures and These results demonstrate that our method produces highly detailed and realistic facial and hand regions of the characters.
Since our method and the baselines are trained different datasets, note that CharacterGen and StdGen are trained on Anime3D (Peng et al. 2024), whereas Hunyuan3D 2.0 is trained on large-scale datasets including ObjaverseXL (Deitke al. 2023). In contrast, our method is trained on proprietary dataset. Consequently, reconstruction- based metrics such as PSNR, SSIM, and LPIPS are directly comparable. Instead, conduct user study that better reflects the per- ceptual quality of the generated results rather than simply computing losses against the ground truth. Specifically,
randomly sample 16 anime-style characters with diverse poses from the Internet as well as from synthesized images, and generate corresponding 3D models. Thirty participants are then asked assess the visual quality of the generated textures and meshes, as well their fidelity to the input images. To mitigate potential bias, participants are instructed focus solely on the perceptual quality of the generated avatars, regardless of pose variations. As summarized in [able the results show that our method consistently outperforms competing ap- proaches.
4.3 POSE CANONICALIZATTIO COMPARISON
The results of the pose canonicalization experiment are presented in Figure 13 (A (Appendix). Our method demonstrates superior generalization compared to prior approaches (Peng et al. 2024 He et al. 2024b Zhao et al.) 2025). We attribute this to several data augmentation strategies employed during training, including random cropping of the lower body, vari- ations in lighting conditions, and adjustments to char- acter contour line thickness.
SMPL
Anine SMPL
Figure 7: Comparison between SMPL and
Anime-SMPL. on Anime Character Mesh Re- construction
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4.4 ABLATION STUDY
Anime-SMPL vs. SMPL. We compare the body mesh predicted by the SMPL model with that generated by Anime-SMPL. model. As shown in Figure the mesh produced by method aligns more closely with the input image in terms of ear shape, facial contour, and relative thigh and calf thickness, In contrast, the mesh predicted by the original SMPL model exhibits noticeable discrepancies in these areas. These results demonstrate that Anime-SMPL more effectively captures the distinctive geometric characteristics anime-style characters.
Body-Aligned Garment Generation.
To verify the effectiveness of the proposed Body- Aligned Garment Generation strategy, we compare the garment-to-bon alignment of models with and without body latent tokens, as illustrated in Fig- ure The results demonstrate that although our MoE-structured Multi-Shape DiT can roughly infer the spatial layout of each garment without explicit body geometry, the resulting garments often exhibit suboptimal alignment and increased susceptibility to interpenetration artifacts. In contrast, the incorpora- tion of body latent tokens provides explicit geometric guidance, allowing the model generate garments that better conform to the body surface. This effect is especially pronounced for tight-fitting garments, such swimsuits, where the generated clothing aligns precisely with minimal interpenetration.
Leann Thlow
In-b
Figure 8: Comparison of Garment Fitting with and without Body Latent Tokens.
MoE-structured Multi-Shape DIT. To demonstrate the effectiveness of the MoE design in our MoE- structured Multi-Shape DiT, we conducted com- parative experiment evaluating the generation perfor- mance of our MULTI-SHAPE DIT with and with- out MoE layers. As illustrated in Figure | the left shows the input image, the middle displays the up- per garments generation results from DiT without MoE layers, and the right presents the up garments generation results from DiT with MoE layers. Our MoF-structured Multi-Shape DiT significantly out- performs the variant without MoE layers in terms of both generation quality and image- geomerry align- ment, thereby validating the effectiveness of our pro- posed MoE layer design.
Figure 9: Comparison of the generation per- formance of our Multi-Shape DiT with and without MoE layers.
APPLICATION
Leveraging our Anime-SMPL model and the Body-Aligned Component-Wii -ise Garment Modeling framework, the generated 3D anime characters can be directly applied to various downstream tasks, including garment retargeting, motion control, and facial expression control.
5.1 3D CHARACTER GARMENT RETARGETING
Leveraging the unified body template of our Anime-SMPL model, we facilitate straightforward and effective garment retargeting across characters. For each vertex on the source garment, we find its five nearest anchor points the canonical body, compute a weighted relative displacement and apply it to the corresponding anchors on the target character to accurately transfer garment geometry. Sample retargeting results presented in Figure 17

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48
10
Betageite
Figure 11: Garment Retargetting results.
Figure 10: Animation results.
5.2 MOTION CONTROL
Leveraging the precise skeletal structure and skinning weights of our Anime-SMPL model, high- quality motion control can be readily achieved. For garments without predefined skinning weights, hybrid approach: body-hugging clothes inherit weights via nearest-neighhor sampling from the body, while garments with greater separation, such as skirts, are animated using physics-based simulation. Animation results are presented in Figure | and Figure [0] As illustrated in these figures, our generated 3D anime characters capture both natural full-body articulation and fine-grained movements, such as delicate finger gestures, thereby underscoring the precision and expressiveness enabled by our pipeline.
5.3 FACIAL EXPRESSION CONTROL
Leveraging the unified body template provided by our Anime- Anime-SMPL model, facial expressions can be controlled by manipulating facial vertices using blend shapes. Representative facial expression control results are shown Figure Furthermore, real-time facial expression control can be achieved via face tracker, demonstrated in Figure 18
6 LIMITATIONS AND DISCUSSION
Although method enables the generation of 3D anime characters from either text prompts single image, several limitations remain. First, the pose canonicalization step in the 2D Canonical- Pose Character Generation stage struggles with characters exhibiting complex poses or multiple accessories, often resulting in distorted outputs. Second, garment meshes extracted from SDFs via the marching cubes algorithm are inherently double-sided which can impair physical simulation. Additionally, our texture generation using a multi- diffusion model suffers from misalignment between projected images and the underlying geometry, well as cross-view inconsistencies. which negatively affect texture quality.
For future work, mesh generation approaches (Chen et al. 2024c Hao et al., 2024) that operate directly on vertices and faces may help mitigate the double-surface artifact. Moreover, generating textures directly in 3D space could alleviate cross-view inconsistency and enhance overall coherence.
7 CONCLUSION
In this work, we present a novel pipeline for generating high-quality, animatable anime-style 3D characters. We decompose each character into set of componentsincluding body, hair, upper garments, lower garments, and accessorieswhich are generated independently. By integrating Anime- SMPL, MoE-structured Multi-Shapp Shape DiT, and a high-resolution texture generation framework, pipeline enables fine- mnne-grined modeling of both geometry and appearance. This design allows the production of high-quality, fully textured, animation-ready anime-style 3D characters.
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ETHICS STATEMENT
This work complies with the ICLR Code of Ethics. Our research focuses on 3D anime character generation from text and images. The study does not involve human subjects, biometric data, any sensitive personal information. We acknowledge the potential risks of misuse of generative technologies, such as producing inappropriate misleading content. To mitigate these concerns, our research is restricted to academic purposes, and we encourage responsible use of our models aligned with ethical guidelines in the community. No conflicts of interest, sponsorship biases, or legal compliance issues are associated with this work.
ACKNOWLEDGEMENT
This work is supported by the Hong Kong Research Grant Council General Research Fund (No. 17213925), National Natural Science Foundation of China (No. 62422606) and Gusu Innovation Entrepreneurship Leading Talents Program (ZXI22243311).
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A APPENDIX
ACKNOWLEDGEMENTS
We sincerely thank the engineering team at DreamTech, especially Jingxi Xu, for their valuable assistance and support throughout this project.
We acknowledge the use Large Language Models for assistance with language editing in this manuscript. All technical content and results the sole responsibility of the authors.
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CharacterGen
StdGEN
Hunyuan3D 2.0
Ours
Figure 12: Mesh comparison between our method and other methods.
Input Image
Charactergen
SIGGEN
Ours
Figure 13: Pose Canonicalization Comparison between our method and other methods.
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Figure 13: Comparison with Rodin. The first shows our results, and the second shows the results generated by Rodin followed by auto- rigging using Mixamo. The first column presents the animation results, and the second column shows the distribution of skinning weights for the shoulder bones.
AnimelDD
Our dateset
Figure 14: Since the Anime3D dataset does not have a unified body template, our component-wise garment model on it. As shown beyond, method shows significant difference in performance between the Anime3D dataset and our dataset.
only train
Figure 16: Visualization of Anime-SMPL: joint structure.
Figure 17: General a-pose skeleton image used in Image-to-Imann Synthesis.
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Figure 18: Keal-Time facial expression control with face tracker.
Figure 19: Visualization of the effect varying B. we vary the first parameter of 3 from -2 to 2 from left to right.
Figure 20: MVAdapter's Color Bleeding. The first row shows the input image on the far left and the result produced by MVAdapter on the right. In the second row, the far left displays the frontal view of the hair decomposed by our decompose model, while the right side shows the MV result obtained by using the decomposed image as input.
Figure 21: Our result on real humans.
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Reviews:
[{"review_id": "LlmtsTxOY4", "reviewer": "Reviewer_JKU9", "summary": "This paper presents Anime-Ready, a novel framework for generating high-quality, controllable, and animatable 3D anime characters from text or a single image. The core contributions are: 1) Anime-SMPL, a new parametric body model adapted from SMPL to better represent the unique and exaggerated proportions of anime characters. 2) A body-aligned, component-wise garment generation pipeline. This pipeline decomposes the character into a body, hairstyle, upper garment, lower garment, and accessories. It uses a novel MoE-based Multi-Shape DiT to generate each garment component's mesh. Experimental results show the proposed method produces siginificant better results than the baselines.", "questions": "1. How does the MoE approach ensure that the generated components (e.g., an upper and lower garment) are geometrically seamless? \n2. Hybrid Motion Control (L452): The hybrid approach for garment animation requires more detail. For simulation-driven garments: What physics simulation engine is used? How are the garment's physical parameters (e.g., mass, stiffness, friction) estimated from the image or text input? Are they simply hard-coded, and if so, how does this generalize?\n3. The paper motivates Anime-SMPL by stating that anime skeletons differ from real human ones. However, the \"Image-to-Image Synthesis\" (pose canonicalization) stage uses OpenPose (L200) to estimate a skeleton for conditioning. Since OpenPose is trained on human poses, doesn't this introduce a contradiction?\n4. In Fig 2, the input image for the character with a tail shows the tail is almost completely occluded. How is the model able to reconstruct it so accurately? Does this suggest potential overfitting to characters present in the training set?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **High-Quality Results:** The method demonstrates a significant improvement over existing baselines (CharacterGen, StdGEN, Hunyuan3D 2.0) in user studies. The qualitative results, especially for fine-grained details, are visually impressive.\n2. **Practical Applications:** The framework is designed for practical use. By generating a rigged (Anime-SMPL) body and separate garment components, it directly enables downstream tasks like garment retargeting.", "weaknesses": "1. **Anime-SMPL Template Details:** \n    - How were the ground-truth shape parameters for the 20,000 characters obtained? Line 235 mentions training a shape prediction network using MSE against \"ground truth,\" but the origin of this ground truth is unclear.\n    - The paper argues that Anime-SMPL is different from SMPL, but provides few details. A visualization of the unified body template's joint structure is needed to understand its topology.  (e.g. number of joints)\n    - How does the unified Anime-SMPL model handle non-humanoid \"accessories\" like wings or tails? Are these part of the body model's joints? If so, how is the model \"unified\" when some characters have these features and others do not (or have multiples)?\n    - Furthermore, a supplemental figure demonstrating the effect of varying the shape (beta) components would be helpful.\n2. **Missing Ablation Studies:**\n    - **MoE-structured DiT:** A central claim is that the MoE-structured DiT (L250) achieves \"precise, component-aware generation\" with \"minimal parameter overhead.\" This claim requires ablation studies to be substantiated. I’m wondering how does this model compare against: (a) training separate, independent DiT models for each of the four components (hair, upper, lower, accessories); (b) a single DiT that does not use MoE, but is instead conditioned on a label token to differentiate the component to be generated.\n    - **MVAdapter \"Color Bleeding\" (L308-310):** The paper mentions that an initial attempt using MVAdapter directly resulted in \"color bleeding.\" A visual comparison showing this failure case should be provided.\n3. **Reproducibility:** The entire method, and especially the core Anime-SMPL model, relies on a large, private dataset of 20,000 characters. The authors do not state whether this dataset or the pre-trained Anime-SMPL model will be released. Without access to either, the results are not reproducible, which is a significant drawback for the research community.", "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": "hwZucjFl6y", "reviewer": "Reviewer_f3Tk", "summary": "This paper proposes a unified framework for generating high-quality, animation-ready 3D anime characters. The authors introduce a parametric anime body model (Anime-SMPL) and design a component-wise garment generation pipeline. The resulting models are partially rigged, skinned, and fully textured. The method outperforms prior work in terms of mesh quality, texture clarity, and garment-body alignment.\n\n**Contributions** \n- A novel parametric anime-style body model, Anime-SMPL, with full skeleton and blendshape rigging for animation.\n- A component-wise garment modeling pipeline that separately generates hairstyles, upper garments, lower garments, and accessories, each aligned to the underlying body.\n- A multi-view texture generation approach that operates per garment component to improve texture fidelity and modularity.\n- Experimental results showing improvements over baselines in mesh quality, garment-body alignment, and texture realism.", "questions": "**Rigging and Animation**\n- What is the dimensionality of the blendshape matrix used in SMPL-Anime? Does the model incorporate separate shape, expression, and pose blendshapes as in SMPL-X, or does it rely solely on shape blendshapes?\n- The paper would benefit from more details on how the Anime-SMPL template is constructed. Specifically, what is the process to obtain rigging components (e.g., blendshapes and skinning weights) obtained? In SMPL, an important factor for learning accurate rigging parameters is that the scans are captured from minimally clothed or tight-clothing subjects. Do you remove garments or otherwise preprocess the 3D anime models to isolate the underlying body before estimating rigging parameters?\n- How is the ground-truth $\\boldsymbol{\\beta}$ (shape) parameter estimated for each 3D anime model?  \n\n**MoE-structured DiT Block**\n\nIt is reasonable to model the four components—hairstyles, upper garments, lower garments, and accessories—separately using a MoE design. However, it would be better to provide ablation results comparing models w/ and w/o the MoE structure. Intuitively, MoE is effective when different experts are activated for distinct inputs or tasks. For example, using upper garment latent tokens would trigger the corresponding expert for upper garments. In this paper, however, both the input image and the noisy garment tokens represent the full body and all garments. As a result, the experts (E1, E2, E3, E4) may process the input holistically rather than specializing, making it unclear how much benefit the MoE structure actually provides.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper introduces a parametric anime body model, Anime-SMPL, learned from 20,000 anime-style 3D models. This contribution is valuable for future research on animatable anime character modeling. \n- A novel pipeline is proposed for generating separate garments and the underlying body, making the outputs easy for editing and good for application. \n- The texture generation approach is also innovative: instead of generating multi-view images of the entire body at once, the model generates multi-view textures for individual garments separately.", "weaknesses": "- From my perspective, the output body is not fully skinned. Only the inner body is rigged and skinned, while body-hugging garments and accessories are bound to the inner body using nearest-neighbor (NN) skinning. The skirt is animated via physical simulation rather than skinning. \n- For tight-fitting garments, inheriting skinning weights via nearest-neighbor sampling introduces hard assignments, which can result in unnatural deformations—particularly in regions with complex articulation or discontinuous topology.\n- The garments are decodered by the VAE decoder, which are independent to the inner body parametric model. This might lead to garment-body intersections or poor fit between the garments and the underlying body. \n- No ablation studies are provided to evaluate the effectiveness of the MoE (Mixture-of-Experts) structure.\n- The paper provides very limited details on how the template Anime-SMPL model is generated, as well as its rigging parameters—including the blendshapes (expression, pose, and shape), joint regressor, and skinning weights.", "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": "L9un6s32nG", "reviewer": "Reviewer_M983", "summary": "This paper proposes Anime-Ready, a framework for generating high-quality, controllable 3D anime characters from text or a single image. The key innovations include: (a) Anime-SMPL, a stylized extension of the SMPL body model that captures anime-specific proportions and provides unified UV maps for texture synthesis; (b) a Multi-Shape DiT with an MoE design for modular garment generation; (c) a body-aligned garment modeling scheme that encodes sampled body points to enforce spatial consistency and reduce interpenetration; (d) a component-wise high-resolution texture generation pipeline using multi-view diffusion and self-attention for disentangled texture synthesis.\nExperiments show improvements in mesh and texture quality over baselines, also supported by a user study.", "questions": "- How large is the parameter difference between Anime-SMPL and the original SMPL? Are blendshape parameters manually designed or learned?\n- Does the body-aligned garment generation guarantee no interpenetration, or are post-processing steps still required?\n- Could the authors release the Anime-SMPL model separately, even without the full dataset, to enable replication?\n- The supplementary video basically shows frontal results. How robust is the canonical-pose generation to unusual camera angles or occluded limbs?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "- The system design is very comprehensive, which integrates parametric body modeling, 3D diffusion, and texture synthesis, addressing multiple practical issues (alignment, rigging, texture bleeding).\n- The novel parametric model is interesting and meaningful. It combines realistic rigging consistency with stylized geometry, enabling animation readiness.\n- Applications such as retargeting and motion control demonstrate that the results are not merely visual but animation-ready.", "weaknesses": "- Quantitative evaluation is limited. The reliance on user studies instead of geometric or perceptual metrics (e.g., FID-3D, CLIP-score, interpenetration rate) makes comparisons less reproducible.\n- The paper uses a private dataset of 20k characters, which may limit replicability. No evidence of generalization to unseen datasets is given.\n- Though not considered as a reason of not accepting the paper, the training requires multiple A100s for ~10 days—resource cost is high for an ICLR contribution. Efficiency or inference speed is not discussed. Also, other components (e.g., MoE expert routing, diffusion conditioning choices) lack separate ablations.", "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": "R4vF3gcspM", "reviewer": "Reviewer_SGSx", "summary": "This paper presents a high-quality 3D anime character generation method. Overall, the proposed method is well-motivated, and the experimental results seems good.", "questions": "I wonder can we use the Rodin to perform 3D avatar generation and then perform auto-rigging like Mixamo. What is the comparison with the proposed method?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "•\tThe paper is well-written with a logical structure that makes the technical contributions easy to follow.\n•\tThe proposed  framework is reasonable and well-justified. The experimental results demonstrate the effectiveness of the approach.\n•\t The demo videos are excellent supplementary materials.", "weaknesses": "•\tRecent works have explored video generation model-based avatar animation  capabilities. A more thorough comparison and discussion of the relationship between ANIME-READY and these methods would strengthen the paper. For example:\n•\tAnimate anyone performs avatar animation via cross-attention and  video generation module. How between ANIME-READY compare to this strategy?\n•\tWhat are the trade-offs between the SDS-loss approach HumanNorm[2], video generation based approach Animate Anyone and the proposed method? Could you clarify when your method is preferable over other menetioned existing techniques?\n•\tCould you please provide more details about the private dataset?\n\n\n1] Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation\n[2] HumanNorm: Learning Normal Diffusion Model for High-quality and Realistic 3D Human Generation", "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)