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Anime-Ready: Controllable 3D Anime Character Generation with Body-Aligned Component-Wise Garment Modeling

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
3D anime character generationstylized body modelingcomponent-wise garment generation
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.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a unified framework for generating animation-ready 3D anime characters by extending the SMPL parametric body model with component-wise garment generation. According to the taxonomy, this work resides in the 'Body-Aligned Component-Wise Garment Modeling with SMPL Extensions' leaf under 'Unified 3D Anime Character and Garment Generation Frameworks'. Notably, this leaf contains only the original paper itself—no sibling papers are present—indicating that this specific combination of SMPL extension for anime characters with body-aligned, component-wise garment modeling represents a relatively sparse research direction within the broader field.

The taxonomy reveals three main branches: sketch-based fashion transfer, learning-based garment recognition, and unified generation frameworks. The original paper's branch (unified frameworks) sits alongside sketch-driven methods that prioritize 2D input control and recognition approaches that extract semantic labels without generating geometry. The taxonomy's scope notes clarify that unified frameworks integrate body and garment modeling end-to-end, whereas sketch-based methods treat garment synthesis as a separate post-process. This positioning suggests the paper bridges parametric body modeling with garment generation in a manner distinct from existing sketch-transfer or recognition-only pipelines.

Among the 22 candidates examined across three contributions, none were found to clearly refute any of the paper's claims. The Anime-SMPL body model contribution examined 10 candidates with zero refutable matches; the MoE-structured garment generation examined 10 candidates with zero refutable matches; and the texture generation pipeline examined 2 candidates with zero refutable matches. This limited search scope—focused on top-K semantic matches and citation expansion—suggests that within the examined literature, no prior work directly overlaps with the specific combination of anime-adapted SMPL extensions, body-aligned component-wise garment modeling, and unified skeleton generation for animation-ready output.

Based on the 22 candidates examined, the work appears to occupy a relatively unexplored niche at the intersection of parametric body modeling and anime-style character generation. The absence of sibling papers in the same taxonomy leaf and the lack of refutable prior work among examined candidates suggest novelty, though the limited search scope means this assessment reflects only the top semantic matches and immediate citations rather than an exhaustive field survey. The taxonomy structure indicates that while related directions exist in sketch-based and recognition-focused work, the specific integration proposed here has not been extensively explored in the examined literature.

Taxonomy

Core-task Taxonomy Papers
2
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: controllable 3D anime character generation with body-aligned garment modeling. The field organizes around three main branches that reflect distinct stages and emphases in the pipeline. Sketch-Based Fashion Transfer and Garment Synthesis focuses on translating 2D design inputs—such as hand-drawn sketches or reference images—into garment geometry, often leveraging neural rendering or style transfer to bridge the gap between artistic intent and 3D output. Learning-Based Garment Recognition and Classification addresses the problem of automatically identifying and categorizing clothing items from visual data, which can inform downstream generation tasks by providing semantic labels or feature embeddings. Unified 3D Anime Character and Garment Generation Frameworks integrate character body modeling with garment creation in a single pipeline, typically extending parametric body models like SMPL to ensure that clothing deforms consistently with underlying anatomy and pose. Within these branches, a central tension emerges between modularity and end-to-end integration: some approaches treat garment synthesis as a separate post-process, while others embed it directly into character generation to maintain geometric coherence. Anime-Ready[0] exemplifies the unified direction by adopting a body-aligned, component-wise garment modeling strategy that extends SMPL representations, ensuring that each clothing piece adapts to character pose and shape in a physically plausible manner. This contrasts with earlier sketch-driven methods like Fashion Transfer[1], which prioritize user control through 2D input but may require additional alignment steps, and with recognition-focused work such as Animated Clothing Recognition[2], which provides semantic understanding but does not directly generate geometry. By situating garment modeling within a parametric body framework, Anime-Ready[0] addresses the challenge of maintaining anatomical consistency across diverse character designs and poses.

Claimed Contributions

Anime-SMPL: Unified Parametric Body Model for Anime Characters

The authors present Anime-SMPL, a parametric body model adapted from SMPL to capture the distinctive geometric features and exaggerated proportions of anime-style characters. This model provides consistent topology, skeletal structure, and UV layout across characters, enabling animation-ready body generation and direct UV-space texture synthesis.

10 retrieved papers
MoE-structured Multi-Shape DiT with Body-Aligned Garment Generation

The authors develop a Mixture-of-Experts based Diffusion Transformer architecture that generates separate meshes for hair, upper garments, lower garments, and accessories. By conditioning on body surface geometry encoded as latent tokens, the model produces garments aligned with the underlying body shape, reducing interpenetration issues.

10 retrieved papers
Component-Wise High-Resolution Texture Generation Pipeline

The authors introduce a texture generation framework that decomposes full-body images into individual garment components using a diffusion model with multi-component self-attention. This approach generates high-resolution textures for each component independently, avoiding color bleeding artifacts that occur when texturing all components simultaneously.

2 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Anime-SMPL: Unified Parametric Body Model for Anime Characters

The authors present Anime-SMPL, a parametric body model adapted from SMPL to capture the distinctive geometric features and exaggerated proportions of anime-style characters. This model provides consistent topology, skeletal structure, and UV layout across characters, enabling animation-ready body generation and direct UV-space texture synthesis.

Contribution

MoE-structured Multi-Shape DiT with Body-Aligned Garment Generation

The authors develop a Mixture-of-Experts based Diffusion Transformer architecture that generates separate meshes for hair, upper garments, lower garments, and accessories. By conditioning on body surface geometry encoded as latent tokens, the model produces garments aligned with the underlying body shape, reducing interpenetration issues.

Contribution

Component-Wise High-Resolution Texture Generation Pipeline

The authors introduce a texture generation framework that decomposes full-body images into individual garment components using a diffusion model with multi-component self-attention. This approach generates high-resolution textures for each component independently, avoiding color bleeding artifacts that occur when texturing all components simultaneously.

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