GarmentGPT: Compositional Garment Pattern Generation via Discrete Latent Tokenization
Authors: Fangsheng Weng
Junhao Chen
Xiang Li
Jie Qin
Hanzhong Guo
ShaochunHao
Xiaoguang Han
Venue: ICLR 2026 Poster (2026)
Keywords: Garment Generation
vision language models
Primary area: generative models

Abstract: Apparel is a fundamental component of human appearance, making garment digitalization critical for digital human creation. However, sewing pattern creation traditionally relies on the intuition and extensive experience of skilled artisans. This manual bottleneck significantly hinders the scalability of digital garment creation. Existing generative approaches either operate as data replicators without intrinsic understanding of garment construction principles (e.g., diffusion models), or struggle with low-level regression of raw floating-point coordinates (e.g., Vision-Language Models).
We present GarmentGPT, the first framework to operationalize latent space generation for sewing patterns. Our approach introduces a novel pipeline where a RVQ-VAE tokenizes continuous pattern boundary curves into discrete codebook indices. A fine-tuned Vision-Language Model then autoregressively predicts these discrete token sequences instead of regressing coordinates, enabling high-level compositional reasoning. This paradigm shift aligns generation with the knowledge-driven, symbolic reasoning capabilities of large language models.
To address the data bottleneck for real-world applications, we develop a Data Curation Pipeline that synthesizes over one million photorealistic images paired with GarmentCode, and establish the Real-Garments Benchmark for comprehensive evaluation. Experiments demonstrate that GarmentGPT significantly outperforms existing methods on structured datasets (95.62\% Panel Accuracy, 81.84\% Stitch Accuracy), validating our discrete compositional paradigm's advantages.
Code is available at \url{https://github.com/ChimerAI-MMLab/Garment-GPT}.

Published as a conference paper at ICLR 2026
GARMENTGPT: COMPOSITIONAL GARMENT PATTERN GEN- ERATION VIA DISCRETE LATENT TOKENIZATION
Fangsheng Weng Junhao Chen Xiang Li
Jie Qin
Hanzhong Guo
Shaochun Hao Xiaoguang Han
ChimerAA SShnnhen International Graduate School, Tsinghua University
"The Hong Kong University of Science and Technology. Guangzhou
School of Science and Engineering. The Chinese University of Hong Kong, Shenzhen
"Scoool of Computer Science, The University of Hong Kong
6 Shenzhen Future Network of Intelligence Institute
Guangdong Provincial Key Laboratory of Future Networks of Intelligenne, The Chinese University of Hong Kong, Shenzhen
Input Image Sewing pattern
Simulation
New Texture
Animation
Figure 1: GarmentGPT generates sewing patterns from text and image inputs, maintaining robustness across arbitrary complex poses and garment textures.
ABSTRACT
Apparel is fundamental component of human appearance, making garment digitalization critical for digital human creation. However, sewing pattern creation traditionally relies on the intuition and extensive experience of skilled artisans. This manual bottleneck signifi- cantly hinders the scalability of digital garment creation. Existing generative approaches either operate as data replicators without intrinsic understanding garment construction principles (e.g., diffusion models), or struggle with low-level regression of raw floating-point coordinates (e.g.. Vision-Language Models). We present GarmentGPT, the first framework operationalize latent space generation for sewing patterns. Our approach introduces a novel pipeline where RVQ-VAE tokenizes continuous pattern boundary curves into discrete codebook indices. A fine-tume tine-tuned Vision-Land Language Model then autoregressively pre- dicts these discrete token sequences instead regressing coordinates, enabling high-level compositional reasoning. This paradigm shift aligns generation with the knowledge-driven, symbolic reasoning capabilities of large language models. To address the data bottleneck for real-world applications, we develop Data Curation Pipeline that synthesizes over one million photorealistic images paired with GarmentCode, and establish the Real-Garments Benchmark for comprehensive evaluation. Experiments demonstrate that GarmentGPT significantly outperforms existing methods on structured datasets (95.62% Panel Accuracy, 8 81.84% Stitch Accuracy), validating our discrete compositional paradigm's 's advantages. Code is available at ht tps: /ttp:://githbe com///o//// Ch--mmAI-MaaLbb Garnet GPT
'Coresponding author
"Equal contribution 'Project Leader *Independent Researcher

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Published as a conference paper at ICLR 2026
INTRODUCTION
The emerging fields of digital humans, virtual reality, and e-commerce have spurred immense demand for high-fidelity digital apparel design. Sewing patterns serve as the foundational blueprint for garment creation, directly encoding geometric and topological information that determines the final 3D shape, fit, and style. However, the creation of sewing patterns is notoriously complex, traditionally reliant the intuition and extensive experience of skilled artisans. This manual bottleneck significantly hinders the scalability of digital garment creation, making the automated generation of sewing patterns from intuitive, multi-modal inputs a critical challenge with profound research and commercial value.
Early attempts ranged from rule-based systems deep learning models reconstructing patterns from 3D scans (Korosteleva & Lee 2022b) or images (Liu et al.] 20236). Recent approaches fall into two paradigms, both operating in "raw data space" without high-level reasoning. First, generative models like diffusion (Liu 2025) excel learning distributions but lack intrinsic understanding of garment construction principles, functioning as data replicators disconnected from symbolic reasoning (Korosteleva & Lee| (022a). Second, Vision-Language Models (VLMs) (Bian et al. 2025 Nakayama et al. (025) face critical mismatch: powerful reasoning engines forced regress raw floating-pon point coordinates, which is a low-level process where errors
accumulate catastrophically.
Our work is inspired by paradigm shift seen in other domains, most notably the success of Latent Diffusion Models (Rombach al. (022). Instead of operating directly in the high-dimensional.
data space, we
first map the problem into more compact, semantically rich latent space. The key insight is that sewing patterns, like other structured geometric data (Chen et al. 2024 Yang et al.] 2025 [Song et al., 2025b), possess an inherent discrete nature that can be effectively captured through vector quantization. Several discretization (Agustsson et al. 2017 Yang et al. 2023 Guo et al. 2025bjapproaches have been explored
related domains by providing learned discrete representations to improve reconstruction quality.
We present GarmentGPT, the first framework operationalizing latent space generation for sewing patterns. A Residual Vector Quantizer VAE (RVQ-VAE) (Zeghidour et al.) 202 I) tokenizes continuous pattern curves into discrete codebook indices representing meaningful components (panels, curves, connections) combinable according to tailoring principles. A fine-tuned Vision- Language Model then autoregressively predicts these token sequences instead of regressing coordinates, enabling high-level compositional reasoning. This paradigm shift allins generation with the knowledge-driven symbolic reasoning capabilities of large language models.
Meanwhile, a major bottleneck for practical garment generation applications is the lack of large-scale datasets pairing real-world photographs with their corresponding precise sewing patterns. This data gap prevents training models for practical use cases involving real photo inputs. To address this critical challenge, we propose a novel data curation pipeline that leverages automated pose variation and appearance editing. The pipeline synthesizes from structured GarmentCode through simulation and rendering dataset of over million paired real human images and garment patterns with diverse poses, fabric textures, and garment fits- RealGGamMM (RG-IM). We also introduce RealGarmentTTexture 164K (RGT-164K), a dataset containing 164K unique flattened garment textures, enabling models to learn from large-scale real-world-lik inputs for the first time. To better evaluate real image-to- lo-ppttern generation, we establish RealGarment-Bent Bench (RG-Bench), a comprehensive benchmark introducing extensive variations in human poses and texture patterns under identical garment patterns, enabling evaluation in manner closer to human usage scenarios.
In summary, our main contributions as follows:
We introduce GarmentGPT, novel generative pipeline built upon Residual Vector Quantization (RVQ) that transforms pattern generation into a knowledge-driven, compositional task in semantic latent space, enabling structurally accurate and editable patterns from multi-modal inputs.
We propose novel Data Curation Pipeline and introduce two foundational datasets synthesized through automated pose variation and appearance editing: RealGarment-IIM (1M+ photorealistic image- pattern pairs with diverse poses, textures. and fits) and RealGarrmen/Texturr-Ilokk (164K unique flattened textures). providing the first large-scale resources for real-world garment generation.

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We establish MeGGGrrmettBeech the first comprehensive benchmark for evaluating garment generation from real human photos, assessing both visual fidelity and structural correctness. Extensive experiments demonstrate our approach significantly outperforms existing methods across all metrics, achieving state-of-the-art performance for garment generation from real photos.
RELATED WORK
2.1 DIGITAL GARMENT DESIGN
Digital garment design spans multiple paradigms. Image-Based Methods such as text-to-image (Baldrati
et al. 2023 Zhang et al.
2022 2024b) and image-to-image translation (Chong et al. 2024, 2025 [Zhang
al. 2025b Chen et al.
2023a) produce photorealistic visualizations but lack structural information for
editing manufacturing 3D Synthesis Methods, including text/image-lo-3D (Poole al. 2022 Chen al.
2025b Long et al. 2024 Miao et al. all [2026] Zhao et al.] 2022: Chen et al. 2 et al. (024c)), video
generation (Wang et al. Karras et al. 2023 Hu 2024, Tu et al. 2025 Chen et al. [2055a] Ma et al.
2025), ) and character animation (Xu et al. 2020 Zhang et al. [202a]] Song et al. 2025a Sun al. [025] Guo
et al. (025c), yield mesh or point cloud outputs that cannot encode 2D panel layouts, stitching, or parametric constraints needed for production. Structured Pattern Representations (Korosteleva & Sorkine-Hornung
2023 Liu al. 2023b) instead encode garments as 2D panels, curves, and stitching relationships, enabling direct use in manufacturing. Our work advances this paradigm via a novel discrete tokenization scheme that preserves structural integrity.
2.2 STRUCTURED DATA GENERATION
Fine-tuning pre- pre-trained foundation models the de facto standard for domain adaptation. NLP, parameter-
efficient techniques such as LoRA (Hu et al. 2022) and prompt tuning (Lester et al. [202]] || Li & Liang. 202T)
yield domain experts like BioGPT (Luo a [2022) and LegalBERT (Chalkidis al. (2020), while efficient
task-specific architectures (Chen et al. 2023b) and comprehensive benchmarks (Zhang et al., 2023 Chen
eet al. 2025c) further drive progress. Vision- Lision-Language Models (VLMs) (Radford et al. 2021 L et al. 2023a
Wang al. 2024a) have shown strong multimodal understanding across diverse tasks (Ye al. [222|] Guo
al. 2025a) and have been applied to structured data generation, including 3D meshes (Wang et al. 2024e
Chen et al. 2024 Tang et al. 2024), skeletons (Song et al. 20256), vector graphics (Yang et al. 2025 Liu
al. 2024b), and CAD (Alrashedy et al. 2024). However, garment patterns pose unique challenges: VLM
token limits conflict with the need for precise geometric constraints and explicit inter- enter-panel topology. Existing
codebook methods (Yu et al. 2021] Ji et al. (2024) do not address these manufacturing requirements; we propose tokenization scheme specifically tailored to sewing pattern geometry and topology.
2.3 SEEWING PATTERN GENERATION
Large-scale datasets like CLOTH3D Bertiche et al., 2020), GarmentCode (Korosteleva et al. 2024) and Dress
Code (Morelli et al. 2022) have enabled data-driven pattern generation through three main approaches: (1) Baaiiiizaato-BBBeeddddd Sensitive Couture (Umetani al. 2011) pioneered bidirectional 2D-3D editing. while FoldSketch (L.i et al.) 2018) enabled pleat design 3D sketches. These methods rely on complex physical optimizations that limit scalability. (2) Autoregressive: GarmentCode (Korosteleva & Dorkinc-Homuug.
2023 uses hierarchical parametric components, while DressCode (Morelli et al. (0022). Alpparel[Nakayana
al. 2025). SewFormerrLLiu et al., 2023b)) ChatGarment (Bian et al.j (0055) employ Transformers for
sequential generation. Despite capturing fine details, they suffer from slow inference and error propagation.
(3) Feed-Forward: StableGarment (Wang al.) (202c) and SewingLDM (Liu et al. (025) diffusion models for parallel generation, offering efficiency but struggling with precise geometric constraints. This precision-cfficiency trade-off motivates our approach.

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3 METHOD
3.1 OVERVIEW
Our garment pattern generation methodology, as shown in Fig. comprises two fundamental components: a quantization module that encodes edge and panel positional in foroaaioon, and sequence generation and editing module based generative Vision-Language Models (VLMs) capable of comprehending multimodal inputs and generating or modifying encoded sequences.
Encoder
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UDS
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edge panel edge
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End Cormert
Edge Lixiten Sampling
Overview of the GarmentGPT framework. Garment patterns are encoded into discrete token sequences via residual vector quantization with separate codebooks for edge geometry and panel positioning, then processed by Vision-Laanuuge Model for multimodal generation and editing.
Figure 2:
We encode edges into discrete index representations through residual vector quantization, replacing tradi- tional continuous numerical representations while simultaneously generating corresponding codebooks for reconstruction. For each panel's translation and rotation parameters SMPL A-pose (Loper et al. 2023)
figuration, we employ an identical imcoing-ddcoding workflow and allocate separate, non-shared codebook distinct from edge encoding.
For complete garments, the encoded edge indices, panel translation and rolation parameter indices, and inter-edge stitching relationships are systematically combined through carefully designed tokenization strategy into sequence data processable by VLMs, termed garment sequences. VLMs can perform multimodal comprehension based images, text, existing garment sequences, subsequently generating editing garment sequences. The generated sequences are then deconstructed through pattern matching methodologies, querying corresponding code tables for decoding, ultimately outputting each garment's dimensions, each panel's translation and rotation parameters, and the positional and stitching relationships of each edge.
3.2 VLM BACKBONE AND TRAINING STRATEGY
We employ Vision Vision-Language Models (VLMs), including LLaVA and QwenVL, foundational architecture for experiments. VLMs convert garment images into structured topological token sequences. These models excel at mapping pixel-level visual features to semantic concepts, making them ideal for perceiving images

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Figure 3: Hierarchical tokenization of garment patterns. A Tank Top garment is converted into structured token sequence using specialized tokens to encode garment structure, panels, positional parameters, edges, and stitching relationships. Detailed encoding process described in Section
and generating structured outputs. We adapt VLMs to garment topological modeling through three key enhancements: (1) Vocabulary Extension: We add specialized topological tokens and codebook indices (0- 1023) as learnable embeddings the LLM vocabulary. (2) Multimodal Input Construction: For generation tasks, we use image-text pairs; editing tasks, we use sequence-texx pairs with editing instructions. Visual textual inputs are processed and fused via projection layers. (3) Training Paradigm: We fine-tune the VLM autoregressively predict topological token sequences from multimodal inputs, minimizing cross-entrops loss against ground-truth sequences.
3.3 QUANTIZATION OF SEWING PATTERNS
Sewing patterns consist of multiple panels with unique identifiers, spatial positioning (translation and rotation A-pose), and contours formed by connected edges. Edges are classified into four geometric types: straight lines, quadratic/cubic Bézier curves, and circular ares, each with specific parameters (vertices, control points, radius, etc.).
Our quantization framework employs three modules eneoder, codebook, and decoder to achieve discrete representation and reconstruction of pattern information. To maintain representational purity, edges and RT volation-translatinn parameters use separate encoder-decoder pairs and codebooks. Refer to the left panel of Fig. for an example.
For edge encoding, we uniformly sample each edge into N points and process them through a lightweight ResNet- besNee-bbsed encoder. RT parameters are concatenated into single vector and encoded using a smaller ResNet architecture. Through Residual Quantization, continuous latent vectors mapped discrete indices via hierarchical codebooks, where vectors exhibit residual relationships. The number of residual levels controls the balance between compression efficiency and reconstruction quality.
During decoding, the system retrieves quantized vectors by querying indices and reconstructs parameters through symmetric decoders. Edge decoders restore endpoints and type-specific attributes (radius for ares, control points for Bézier curves via lossless quadratic- to-cubic conversion). RT parameters directly regress to continuous values, enabling high-fidelity reconstruction of complete pattern structures.
For more details, please refer Appendix Sec. C
3.4 TOKENIZATION
A pivotal aspect of fine-tuning VLMs lies in effectively representing the geometric topological structure of garment patterns as token sequences. To this end, we have designed a specialized garment pattern topological representation framework that introduces set of special tokens T to encode different hierarchical levels and relationships within garment structures:
T (SoG), (EoG), (SoP), (EoP), (SoL), (EoL), (SoE), (EoE), (SoS), (EoS), (ESEG))
(1)

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Each token pair marks the beginning and end of specific semantic units, corresponding to the overall garment, panels, positional parameters, edges, and stitching relationships, respectively; additionally, the (ESEG) token serves as delimiter to distinguish index sequences of different edges within the same panel.
Hierarchical Structure. This representation methodology employs a hierarchical organizational structure, as illustrated in Fig. 3 The complete garment commences with (SoG), followed by dimensional information, all panel data, and strtching relationships, ultimately concluding with (EoG). Each panel's information initiates with (SoP), encompassing its identifier, positional parameters, and edge sequences arranged according to drawing order, terminating with (EoP). Panel positional information is denoted by (SoL.), succeeded by index values organized in aa, b, c) format, and concludes with (EoL). Edge information within panels begins with (SoE), followed by corresponding index sequences in the codebook, with (ESEG) separating indices different edges, and the entire edge information terminates with (EoE). Stitching information is encapsulated between (SoS) and (EoS), specifically describing edge pair relationships requiring stitching.
Concrete Example. Fig. iillustrates tokenized representation of Tank Top garment. This garment measures 93 centimeters dimension (defined as the maximum absolute value of all panel vertex coordinates), comprising panels (right_big _ttoss, seft_btoro, left_ fffosfoooo,, and fohh_ftoroo), with each panel consisting of 13 edges. The garment exhibits various stitching relationships, including edge connections within the same panel (e-g., the 1tth and 12th edges of right ftorso) and between different panels (e.g., the edge of right btorso and the 4th edge of left btorso, shown in identical colors). In this example, we use 5 residual indices for edge encoding and 3 for positional parameter encoding, with each index ranging from O to 1023 (codebook size of 1024). Through this tokenization methodology, the complete garment transformed into the discrete token sequence shown in the figure.
3.5 LOss FUNCTION
We design separate loss functions for the quantizer and VLM components.
Quantizer Loss. For the quantizer component, the overall loss comprises multiple weighted sub-losses to comprehensively assess encoding and reconstruction quality:
Lquare AeteCals + Aventex Evertex + Accetral Coontoll + Acommir Ccommir
(2)
where Adts Avertes Acontrot, and Acommit represent the weighting coefficients for each loss component. Each component is defined as follows:
Classification Loss (Car): This loss accurately predicts the geometric type of each edge (straight lines, Bézier curves, or circular arcs) using standard cross-entropy loss.
Vertex Loss (Everte):: This loss constrains reconstruction precision of curve endpoints by computing the L distance between predicted and ground-trunt truth endpoints:
Evertex = || vallã
(3)
where Vprd and Vgr denote the predicted and ground-truth vertex coordinates, respectively.
Control Point Loss (Ceoatra):: This loss computed differentially for distinct curve types. For straight lines,
we use two trisection points as proxy control points. For Bézier curves, we calculate the L2 distance between predicted and ground-truth control points. For circular ares, the loss comprises three components: (I) by distance for the radius parameter, (2) binary cross- cross-entropy loss for the major/minor are indicator, and (3) binary cross-entropy loss for the drawing direction:
Gernral = ||r rgull2 + BCE dg) + (CE((a ag)
(4)
where and represent the radius, drawing direction, and major/minor are indicator, respectively.
Commitment Loss (Lcommit:: This loss enhances correspondence relationships between encoding vectors codebook entries, driving continuous codebook updates during training:
Leammit = 3. - sg((=))
(5)
6

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where &e (x) denotes the encoder output (continuous feature vector), 2y represents the quantized codebook vector obtained through neiiest search, sg(-) denotes the stop- nop-grradent operation, and | is hyperpa- rameter controlling the commitment loss weight.
VLM Loss. For the VLM component, we employ the standard cross-entropy loss function to maximize the model's conditional likelihood probability for target sequences. This loss operates through autoregressive methodology, predicting the probability distribution of the next token based on previously generated tokens:
N LVLM Σ log (token; tokenca, C)
(6)
where C represents the conditional input context (such images, text, or structured sequences), and N denotes the total sequence length. This loss function effectively drives the model to learn complex mapping relationships between multimodal inputs and structured garment sequences, constituting core mechanism for achieving sequence generation and editing functionality.
DATA CURATION PIPELINE
Existing Image-to to Po-Gaammentooee methods trained on SMPL-renderes datasets exhibit poor generalization on real photos. While substantial GarmentCode data exists, obtaining corresponding real human images scale remains primary bottleneck training amage-conditiones generation models.
Poee Besting Scase
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Texture
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Namo burana Results
Sin/lation & handering
Reol Mhete Traester
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Figure 4: Data curation pipeline overview. Our pipeline generates photorealistic human images from GarmentCode through texture extraction, motion-aware simulation, photorealistic translation, and quality filtering.
We propose an automatic data curation pipeline that generates photorcalistic human images from GarmentCode while preserving garment structure consistency (Fig. D.. Fig. 55) illustrates representative examples from dataset, showcasing the smooth interpolation process from A-pose to diverse poses and the resulting photorealistic human images. The pipeline consists of four stages: (1) Texture extraction and augmentation: We build a texture extraction pipeline using Grounded-SAN, (Ren et al. 2024) and FabricDiffusine (Zhang al. 2024a) process existing datasets (Liu et al. 2016, HHang al. [2020] Cimpoi al., 2014 Shakin Topal 2022: Liu et al. 2024a), yielding RGT- 164K with 164K unique texture maps. (2) Motion-aware
simulation: We extract SMPL poses from AMASS (Mahmood et al.) 2019) and render clothed human videos with varying poses and textures using GarmentCode (Korosteleva & Dorkine-Hornung 2023) and Contourcraft (Grigorev al. 2024). (3) Photorealistic translation: Selected keyframes are converted to photorealistic images via Qwen-image-edit (Wu et al. (025a), ensuring garment structure consistency

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through physics-basee simulation. (4) Quality filtering: detachment detection, visual plausibility assessment via VLM, and coverage verification, improving alignment acceptability from 64.3% to 99.6%.
We implement multi-stage filtering including garment
This pipeline produces RealGarment- IM (RG-IM), containing over one million photorealistic human image- GarmentCode pairs with diverse textures, poses, and appearances. See Appendix Sec. Sec.AAfor implementation details and dataset statistics.
Figure 5: We use A-pose as the first frame and the target pose the last frame for easing interpolation. For each case, the leftmost real human photo converted from its adjacent rendering.
5 EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Our RVQ- VVQ encoder consists of an 8-layer ResNet backbone, which maps continuous boundary curves (lines, circular arcs, and cubic Bézier curves), each sampled into 32 points, into discrete tokens. We codehook of size 1024. The codee is trained on dataset of approximately generative component, we fine-tune Large Vision-Langusge Language Model (LLaVA-1.5 1.5 7B and Qwen-2.5-VL. on curated dataset. The model is trained for 18 epochs with sequence length of 4096 for LLaVA-1.5 7B and 16384 V.L.-
million curve segments. For the
5.2 BASELINES AND METRICS
We use two primary evaluation settings: (1) GarmentCode Dataset. A large-scale structured dataset containing GarmentCode representations paired with corresponding rendered images and textual descriptions. This used for direct comparison with baselines such Alpparel and ChatGarment. Real-Garments Benchmark. novel benchmark created using our Data Curation Pipeline, featuring over 2,000 photorealistic images of virtual humans in diverse poses, paired with their ground-truth GarmentCode. This is used to evaluate performance real human photo inputs.
We evaluate of the-ar art GarmentCode generation models, including SewFormer ChatGarment (Bian et al. 2025), and Alpparel (Nakayama et al. 2025)
Liu al. 2023b

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We evaluate performance using a comprehensive set of metrics, Structural Accuracy is measured by Panel Accuracy, Edge Accuracy, and Stitch Accuracy. Geometric Error is calculated as the L2 distance for Panel Vertices, Panel Midpoinis, Rotation, and Translation. For our Real-Garments Benchmark, we additionally report Practical Precision using Vertex Accuracy @ X mm, which measures the percentage of vertices within specified millimeter tolerance of the ground truth. These metrics are consistent with the Alpparel (Nakayama) 2025) setting.
5.3
EVALUATION ON GARMENTCODE DATASET
In this section, evaluate GarmentGPT's performance the structured GarmentCode dataset and compare against the state-of-the regression-bent based model, Alpparel.
Superiority over Continuous Regression. As summarized in Tab. GarmentGPT sigmificantly outperforms Alpparel in the multi-modal (Image+Text) setting. We achieve 95.62% Panel Accuracy compared to Alpparel's 78.92% (+16.7%) and 81.84% Stitch Accuracy versus their 56.57% (+25.3%). This stark difference highlights the fundamental advantage of our discrete, compositional paradigm. By reframing generation as token selection task, GarmentGPT leverages the VLM's symbolic reasoning strengths, avoiding the error accumulation inherent in regressing thousands of continuous coordinates.
Effectiveness of Multi-modality and Editing. Our ablations confirm that GarmentGPT effectively fuses multi-mode inputs. The combination of image and text yields the best results, improving Panel Accuracy from 93.53% (image-only to 95.62%. Furthermore, the model's 's exceptional performance on the editing task (c.g., 92.95% Stitch Accuracy) demonstrates that our discrete representation provides a semantically meaningful and robust structure for gramular modifications.
Table I: Main results on the structured CarmentCode dataset.
Methol
Sedting/ Backhone
Panel Are.
Fdlgs Aec.
Stätch Aec.
Vertieus 1.2
Midlouint 1.2
Rotation 1.2
Tramsitiin 1.2
Baselines
ChatGoment
60.22%
42.125
40.21
30.15
1910.42
10.51
10.15
Alppoed
78.92%
74.31%
$6.57%
25.55
793.30
3.87
5.22
Test-oely
64.05%
76.71%
51.165
4k
1507 18
0.84
8:0
Image-anly Imag Editing
93.535
$0.755
80.08%
17.35
$43.86
0.56
293
98.62%
90.40%
81.84%
18.43
577.95
0.39
305
93.80%
94.02%
92.95%
11.07
253.1
0.97
293
Backhone Ablation
Qwen- 36
85 85.56%
$1.32%
68.13%
733.22
23617.66
0.77
5.09
Qwen-71 76
90.31%
65.79%
74.77%
46 09
1464 72
0.67
4
Qwen 32B
91.05%
$3.15%
75.8%%
$1.20
1390.15
0.72
399
5.4 EVALUATION ON THE
BENCHMARK
We evaluate generalization to real- world scenarios using our Real-Garments Benchmark, which features over 2,000 photorealistic images with diverse poses and appearances. This presents significantly harder task than the clean A-pose renderings in GarmentCode. All methods are trained our curated real photo dataset for fair comparison.
As shown in Tab. 2] all methods experience performance degradation, validating the benchmark's 's difficulty. However, GarmentGPT maintains clear superiority: while baselines suffer severe drops (ChatGarment: 25. 34%. Alpparel: 38.76% 76% Panel Accuracy), our method retains approximately 95% of its performance, achieving 90.84% Panel Accuracy. a 2.3x improvement over the best baseline. This demonstrates that discrete tokenization learns robust, pose-invariant representations, establishing GarmentGPT the first practical solution for real-world garment digitization.
Table 2: Performance comparison the challenging Real-Garments Benchmark.
Method
Setting
Punel Acc.
Edge Acc.
Stitch Acc.
Vertices L.2
Midpoint 1.2
Rotution L.2
Translation 1.2
Baselines
CharCiammnt
25. 34%
17,82%
45%
71.2%
4523.67
24.35
26.18
Alpparel
38.76%
11.25%
27.34%
$8.92
1842.76
9.47
12.85
GTrmentGPT (LLAVA-L.5)
Image only
88.67%
84.28%
76.34%
19.45
612.87
0.63
3.28
Text
90.84%
85.92%
77.56%
20.67
668.23
0.66
3.42

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5.5
QUALITATIVE RESULTS
Fig. preseens qualitative comparisons between SewFormer, ChatGarment, Alpparel, and GarmentGPT real human photo inputs. While baseline methods produce incomplete or distorted patterns, GarmentGPT consistently generates accurate, well-structured results that closely match the ground truth. Additional results further demonstrate method's robustness across the Real-Garments Benchmark (Fig-) Fig. TO Fig.
[T]
diverse poses, garment types, and textures.
Additionally, Fig- Tig--dees demonstrates potential applications ulation, texture editing, and animation. Our method enables accurate garment pattern reconstruction from arbitrary real human photos, facilitating downstream workflows in garment design and manufacturing.
MambennGTT-ggenrrttte patterns in physical sim-
Image
Seuformer
ChatGarment CVPR 25
Alpparel
GermensGPP (Durs)
Figure 6: Each shows the front and back renderings of garment code generated by different methods. The input images first row are real human photos. The left four cases use real human photos converted from SMPL renderings, while the right two cases use human photos from the Internet.
5.6 ABLATION STUDY ON RVQ- VAE CODEC
We validate the of framework by ablat- ing the number residual quantizers (Q) in RVQ-
Table 3: Ablation study on the number residual quan- tizers in our RVQ-VA Q-VAE codec.
VAE. As shown in Tab. 3 increasing Q from to 8 drastically improves reconstruction quality: total loss drops from 3.72 to 0.08, vertex loss decreases from 5.9 10-3 to 0.05 10-3, and curve accuracy reaches 99.8 99.8%. This confirms that Q=8 enables liable pattern tokenization.
Metric
Q-5
Total Loss
3.72
0.36
0.15
0.08
Vertex Loss 10-3)
5.9
0.39
0.14
0.05
Curve Acc.
CONCLUSION
GarmentGPT introduces discrete tokenization via RVQ-VAE for sewing pattern generation, achieving 95.62%
Panel Accuracy on structured data and 90.84% on real photos. We contribute RealGarment- 1M (IM+ photorealistic pairs), RealGarmennTexxure. ready pattern generation from casual photos. This democratizes garment design beyond expert artisans.
and Real-Garments Benchmark, enabling manufacturing-
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SCKNOWLEDGEMES
The work supported in part by NSFC with Grant No. 62293482. the Basic Research Project No. HZQB- KCZYZ-202 1067 Hetao Shenzhen-HK S&T Cooperation Zone, by Guangdong Provincial Outstanding the Shenzhen Outstanding Talents Training Fund 202002, the Guangdong Research Projects No. 20172107X152 and No. 2019CX01X104, the Guangdong Provincial Key Laboratory of Future Networks of Intelligence (Grant No. 2022B1212010001), and the Shenzhen Key Laboratory of Big Data and Artificial Intelligence (Grant No. XYSPG2022122111783332222 the Guangdong
Youth Fund with No. 1023B151500005
Province Radio Science Data Center.
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Zibo Zhao, Zeqiang Lai, Qingxiang Lin, Yunfei Zhao, Haolin Liu, Shuhui Yang, Yifei Feng, Mingxin Yang. Sheng Zhang, Xianghui Yang, al. Hunyuan3d 2.0: Scaling diffusion models for high resolution textured 3d assets generation, arXiy preprint arXiv:2501. 12202, 2025.
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A DATA CURATION PIPELINE
Existing Saage-to-GarmeniCCoo methods trained SMPLL-rendered datasets exhibit poor generalization on real photos. While substantial GarmentCode data exists, obtaining corresponding real human images at scale remains the primary bottleneck for training image-conditioned GarmentCode generation models.
A.1 PIPELINE OVERVIEW
To address this challenge, we propose an automatic data curation pipeline that generates photorealistic human images from GarmentCode while preserving garment structure consistency. Our pipeline consists of four main stages: (I) texture extraction and augmentation, (2) Simulation and rendering with variations in human pose, garment structure, and texture, (3) mmage-to-image translation for photorealism, and (4) quality filtering.
Challenge: Pose-Induce Structure Drift. Direct pose editing using existing text-conditioned (Wu et al.
2025a
Google 2025 Wang al.] 2025a Hurst al., 2024: Wu et al. 20255 Labs al., 2025 Xie al.
2025)
or pose-conditioned (Tu et al. 2025: Wang el al. 2025bb Xu et al. 2025; Wang al. [2024b Hu
2024
Sun al. [0244 Chong et al. 2024]Chor Choi et 2024 Kim et al. [024] Fang et al, [2024] Velioglu lu et al.
2024
Chang et al. 2023 Morelli al. 2023) image editing models alters garment structure. For example,
as shown Fig. (left), fitted cheongsam transforms into flowing skirt after pose conversion. To ensure
structure consisteney, we render SMPL--baed clothed human videos by varying human motion poses and garment textures through physics-basee based simulation (Grigorev al. 2024). then convert selected keyframes photorealistic images.
Texture Extraction and Augmentation. We built a texture extraction pipeline using Grounded-SAM (Ren
et al.
2024) and FabricDiffusion (Zhng et al. [2024a] to process existing datasets, including DeepFashion (Liu
al. 2016), DeepFashion- MoppFashoonMuluiMddaall (Huang all 2020). DTD (Cimpoi et al.) 20144) TFD (Shakir
Topal 2022), and FID (Liu et al. 2024a). We use "coohhnng" as the text prompt to segment the garment
region and obtain the corresponding mask, from which select the largest inscribed square. As shown in the "Texture Patch Selection" step Fig. + selecting the largest inscribed square within the mask region filters out segmentation failures (e.g.. the segmentation mask of the black top fails in the Human Images input). We then employ FabricDiifusion (Zhang et al., (024a) extract flattened texture maps from clothed human and garment images. This process yields RGT-164K dataset. containing 164K unique texture maps (see Tab. for details).
Motion Augmentation and Simulated Rendering. For each garment pattern in GarmentCode, we perform data augmentation by varying garment textures and human poses. We extract random SMPL poses from AMASS (Mahmood al.) 2019) and generate smooth motions from the standard A A-pose to the random poses through cosine interpolation. This ensures natural pose transitions while maintaining garment structure and preventing penetration artifacts during simulation. Using GarmentCode (Korosteleva & Porkine-Hornung 2023) and Contourcraft (Grigorev et al. 2024), we render S.MPL-based clothed human videos with the above variations in a simulation environment. Each video starts from the A A-pose as the first frame and ends at the selected random pose frame. We select the first frame (A-pose), middle frames, and the final frame (the pose sampled from AMASS) for the next stage.
Translation for Photorealism. We employ Qwen-image-edii (Wu et al. (025a) to convert rendered SMPL-based images into photorealistic human images. This translation step bridges the sim-to-real gap preserving garment structure, as the underlying 3D simulation ensures consistent garment topology across pose changes. The text prompt used is shown in Fig.
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Trans form the SMP1, model this Image into photorea istic human, while keeping the body pese, full-body clothing style, texture, cut, size, and dimensions unchanged.
Figure 7: Text prompt for the Image Editing model.
A.2 DATA FILTERING
To ensure dataset quality, implement multi-stage filtering pipeline to remove failed cases from the rendered images across different poses:
Stage I: Garment Detachment Check. We examine the garment mesh from the simulation to detect garment slippage in the final frame. Specifically, we filter out cases where the highest point of the garment mesh below the height the SMPL root node, indicating garment detachment.
Stage 2: Visual Plausibility Assessment. We employ Qwen3-max API (Wang al. 2024a) to assess the visual plausibility of rendered images. The VLM filters out implausible cases such as garment slippage, exposure of private body parts, or missing garment components. The text prompt used is shown in Fig. 8
Rva luate whether this Image shows à persen wearing clothing appropriately and naturally. Please check for the follewing issues: (1) garment slippage or falling oft the body, (2) inappropriate exposure of private body parts, (3) missing or incomplete garment components, Based on your assessment, answer 'accoptable" if the clothing appears normal and appropriate, or "unacceptable" if any of these issues are present.
Figure 8: VLM evaluation prompt for quality filtering.
Stage 3: Garment Coverage Verification. We Grounded-SAL (Ren et al. (024)) with "clothing" as the text prompt to verify that the segmented garment region adequately covers the human body. We filter cases where the Intersection over Union (IoU) between the garment mask and the human body mask is below 50%.
Validation. To validate that RealGarment dataset closely approximates real photo inputs, conduct two validation studies. First, we randomly sample % of the generated rendered images for manual inspection, confirming 99% rendering success rate. Second, we design user study to evaluate garment-patted alignment quality between photorealistic human images and garment pattern renderings. We randomly sample 1,000 images and ask human evaluators to assess alignment acceptability both before and after applying our filtering pipeline. Results show that our filtering pipeline significantly improves alignment acceptability from 64.3% (743/1000) 99.6% (996/1000), demonstrating a substantial improvement of 35.3 percentage points in data quality.
A.3 DATASET STATISTICS
Based on our proposed data curation pipeline, we synthesize RealGarment-IM (RG-IM), a dataset over one million photorealistic human image-Gane -ange-GGmeenCooee pairs. Combined with RGT-164K, our datasets provide diverse textures, poses, and photorealistic appearances for training robust image-conditin coneecoondt GarmentCode generation models. Sample examples from RealGarment- 1M dataset shown Fig. Detailed statistics of texture-related datasets are shown in Tab. 4
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Table 4: Comparison between clothing texture-related datasets and our proposed RealGarmeenTTexture-66666
dataset
Dataset
Type
Size
Description
Texture Datasets
Textures Dataset (Huang et al. 2020
Texture
8.7 K
General texture images
DTD Cimpoi et al. (0144)
Texture
5.6 K
Describable textures database
TFD Shakir & Topal 2022)
Texture
3 K
Ten fabric categories
FID (Liu et al. 2024a)
Texture
12.2 K
Wool fabric
Raamenrment' Texture- (Ours)
Texture
164K
Garment Texture
Garment Datasets
Clothes Dataset (chussboi96)
(chussboi96
2024
Garment
0.1
Clothing images
Clothes Dataset (ryanbadai)
[Badai] 2023
Garment
7.5 K
Clothing images
Clothes Dataset (agrigorev)
Grggree 2020
Garment
7.5 K
Clothing images
Dress Code (He 2024
Garment
53.8 K
Clothing images
Fashion with Human Body Datasets
DeepPashion (iu et al.
2016
Human
44K
Full- Full-body fashion images
B
MORE RESULTS
We present results generated by GarmentGPT. Using real human photographs as input, the corresponding original SMPL renderings are shown in the first row of each case. Please refer to Fig. Fig. 10, and Fig. 11 Fig. demonstrates GarmentGPT's results inputs with complex textures.
DETAILED QUANTIZATION OF SEWING PATTERNS
Through a comprehensive analysis of sewing patterns, we discovered that their structure consists of multiple panels interconnected by stitching relationships among various line segments. Each panel possesses a unique identifier, spatial positioning A-pose configuration (encompassing translation and rotation parameters), a contour formed by consecutively connected closed edges. Each edge can be classified into four fundamental geometric types: straight lines, quadratic Bézier curves, cubic Bézier curves, and circular ares. All edges share vertex parameters as foundational geometric information; quadratic and cubic Bézier curves incorporate one and two control point parameters, respectively; while circular ares additionally encompass radius, drawing direction, and major/minor arc indicators attributes. Through systematic encoding and high-precision reconstruction of this information, complete garment patterns can be faithfully reconstructed.
In our methodology, the quantization framework constitutes a comprehensive processing pipeline comprising three core modules: encoder, codebook, and decoder, designed to achieve efficient discrete representation and reconstruction of edge information and RT (translation-rotation parameters within garment patterns.
To preserve the representational purity of distinct modal information, edges and RT parameters employ two independent sets of pecoder-decodee pairs and codebooks, ensuring that the two information types remain mutually non-interfering during encoding and reconstruction processes. For edge information, to accommodate real-world design scenarios where precise control points are frequently absent, we uniformly sample each edge into N points as input. Regarding model architecture, we employ a ResNet-basee edge encoder, whose lightweight, flexible, and efficient characteristics are particularly suitable for rapid training and generalization under large-scale datasets. For each panel's RT parameters, we concatenate translation and rotation information into a unified vector serving as RT encoder input. The RT encoder similarly adopts ResNet architecture, but with significantly fewer parameters compared to the edge encoder, matching its relatively lower data complexity.
Following the acquisition of encoded continuous representations, we map them to discrete indices through codebook mechanisms, thereby achieving effective quantized representations. We maintain independent
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Figure 9: We show the results generated by GareenGPP.. Real human photos are used as input, with the corresponding original SMPL renderings shown in the first row of each case.
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F
Figure 10: show the results generated by GarmentGPT, Real human photos are used input, with the corresponding original SMPL renderings shown in the first row of each case.
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Image Editing for Complex Texture
Figure 11: We show the results generated by GarmentGPT using real human photos input. The corresponding original SMPL renderings are shown in the first row of each case. GarmentGPT maintains high success rates even with complex texture inputs.
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codebooks for edges and RT parameters separately, and transform continuous latent vectors into discrete symbols through lookup table operations. Specifically, employ Residual Quantization methodology construct codebooks, utilizing multiple hierarchical indices to jointly represent an edge or RT parameter, where codebook vectors corresponding to cach index exhibit residual relationships. By adjusting the number of residual hierarchy levels, thus achieving flexible halance between compression efficiency and reconstruction quality.
control the representational capacity of latent vectors at different gramularities,
Ultimately, during the decoding stage, the system retrieves quantized latent vectors by querying discrete indices and reconstructs original parameters through symmetric ResNet decoders corresponding to the encoders. For edges, the decoder restores endpoint parameters and processes remaining attributes according to curve type distinctions: circular ares require prediction of radius, drawing direction, and major/minor are characteristics, while Bézier curves simplify parameter prediction through lossless conversion from quadratic to cubic forms. RT parameters directly regress their original continuous values, thereby accomplishing high-fidelity reconstruction from discrete representation to complete pattern structure.
D THE USE OF LARGE LANGUAGE MODELS (LLMS)
Large language models (L.LMs) were used only to assist with language editing and minor text improvements in the preparation of this manuscript. They were not involved in the design of the research, the development of methods, the execution of experiments, the interpretation of results. All scientific content, including analyses, conclusions, and contributions, remains the work the authors.
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Reviews:
[{"review_id": "vvWexquyBc", "reviewer": "Reviewer_EWew", "summary": "This paper considers the task of generating sewing patterns conditioned on an image of a person wearing a garment. To this end, a novel Real Garments Benchmark is established which re-purposes existing GarmentCode dataset to create pairs of realistic garment images and accompanying patterns. The garment images are obtained by taking GarmentCode models, rearranging them to different poses and augmenting the garments with textures obtained from real-world images and texture generators.\n\nAs for the method that generates sewing pattern descriptions based on images, a pipeline based on residual vector quantization and vision-language models is proposed. The sewing pattern is encoded by two resnet based networks: (i) the first resnet encodes points collected along individual panel edges, (ii) the second network encodes rotation and translation parameters of individual panels that are concatenated. These encodings are discretized with residual quantization. A decoder network reconstructs the panels based on the quantized representations.\n\nThe patterns may be represented with tokens: each garment/panel/edge is defined by the starting and ending tokens, and discretized representation allows for the physical properties to also turn into tokens. This representation enables training of vision-language models, where sewing pattern prediction becomes a prediction of the correct garment token sequence conditioned on the input image.", "questions": "[Q1] Explaining some of the elements of the problem would make it more friendly for readers not in the domain: e.g. what is A-pose, rotation and translation parameters of the code and why they are important etc.\n\n[Q2] Data curation pipeline is badly described. The citations are not separated from the text (lines 283 - 300) and this should be fixed. I am still not completely clear on the way that the dataset was generated. For now it seems that these are synthetic images generated by a 3D simulation where the texture of the model and the garment may be controlled. I am not sure I would call this real human images, though they are probably better at capturing true garment behaviour. \n\n[Q3] Is there a theoretical limit to the edge variablity that may be expressed by the quantization approach?\n\n[Q4] Is this approach sensitive to the order of the panels during training? What indicates which patterns should be sewn together and how is that encoded?\n\n[Q5] Could the dataset be extended to include e-commerce image of the garment? That would make the dataset valuable for the further development of tasks such as Virtual Try-On and Virtual Try-Off.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "[S1] The effort of creating a large scale dataset is appreciated, especially as good benchmarks drive development of better models.\n\n[S2] The idea of reformulating pattern descriptions as a sequence of tokens that gives a hierarchical representation of the garment is interesting.", "weaknesses": "[W1] I am still a bit uncertain about the use case of generating a garment pattern from a single image of a person wearing the pattern. If there is a person wearing an item of clothing, that means that the pattern already exists. Also, it is impossible to predict panels on unseen regions of garments (e.g. the back). The success of the proposed method suggests an overfitting to the training dataset.\n\n[W2] Somewhat connected to the previous point, the paper would benefit from additional experiments stress testing the method and connecting it to potential real-world uses. E.g. what happens when the method is applied to real images of people in images captured in the wild? Are the produced patterns reasonable? Are the patterns rasonable for an identical garment worn by people of different sizes? How far can the edge of the panel be warped before it cannot be represented by the codebook? Will an edge be well represented if it is not in a training data (e.g. a model is trained on only straight edges, but a curvy edge appears in the test data)?\n\n[W3] The paper would benefit from qualitative results. I am especially not clear on what garment editing is or how it is evaluated.\n\n[W4] I find the dataset itself the biggest contribution of this paper, but it is very badly described (see questions).", "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": "j62VwekbwY", "reviewer": "Reviewer_HvT3", "summary": "The paper proposes GarmentGPT to address the challenge of generating structured garment sewing patterns from inputs like text or images. It proposes a two-stage framework that first tokenizes garment geometry into discrete latent codes using a Residual Vector Quantized VAE and then trains a VLM to sample or edit these token sequences auto regressively. A hierarchical token schema is proposed (garment > panel > edge > stitch) to ensure that the generated output respects garment topology. Edge curves and pose are encoded into separate codebooks to disentangle geometric factors. The contributions are a novel discrete token representation of garment structures that enables compositional reasoning and editing, the GarmentGPT model for generation and modification of sewing patterns, and a large-scale dataset to evaluate structured garment generation from real photos.", "questions": "1. Referring to Weakness 1: Why do I need to create parametric garment-codes for digital human avatars? Is this a necessary contribution to the field of digital avatars?\n2. Referring to Weakness 2: For real-world applications, i.e. extracting the garment codes from photographs, do the method-generated patterns capture the actual complexity of garment designs? What about different types of seams that are required for stitching together certain parts of a garment? \n3. I find the order of subchapters in chapter 5 a bit unconventional. Why do the ablations come before the evaluation of the experiments? I would move the ablations to the end of the chapter. This would also highlight the importance of current Table 2 (the main quantitative results) over current Table 1.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 1, "strengths": "- The idea of encoding garment structure as a hierarchical sequence of discrete tokens innovative and aligns with current research directions in llms to enable structured reasoning over geometrical data compositions. The model design is conceptually consistent and technically well-motivated.\n- The framework supports not only garment generation from text or images but also editing and refinement of existing GarmentCode sequences, making it more practical for real design applications.\n- The introduction of a large-scale, multimodal dataset and benchmark is a strength.\n- The approach demonstrates improvements over existing, regression-based method.", "weaknesses": "1. I find the motivation for the proposed contribution difficult to understand. Does the method solely work for creating garments for digital avatars? What about creating patterns for real-world garments? \n2. To the best of my knowledge, real patterns are much more complex than the examples shown in the paper as they often exhibit fine-grained textile features for seams or laminated strips. It appears to me that the method does not capture this complexity. At the very least, this is difficult to assess without substantial qualitative examples.\n3. There are almost no qualitative results shown in the main paper or the appendix. Highlighting visual results on both the GarmentCode and Real-Garments datasets would be essential to assess the contribution and current capabilities of the proposed method. It would help the understanding in multiple ways as it a) shows how practically applicable the method is by showing what complexity of garment designs can be extracted/generated from the inputs,  b) visualizes the setting and difficulty of tasks in the benchmarks, c) allows to draw qualitative comparisons between the proposed method and baselines.\n4. The quantitative results on the proposed real-garments benchmarks make the contribution somewhat questionable. I acknowledge the authors disclose the performance drop and pose this as an open challenge for future research, but doesn’t this show that the method not really works for the intended application? I would also like to see the metrics for the other baseline methods on the real-garments benchmark.\n5. The authors do not provide any information about the cost (necessary GPU resources) of their method. This information would be important to assess how this be scaled and improved, e.g. to capture more complex patterns and finer details. \n\nMinor remarks:\n1. Figure 1 has a fairly low resolution, making it hard to read when zoomed in. Please consider embedding it as pdf or svg for better quality.", "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": "Guka4DdcJK", "reviewer": "Reviewer_e7jR", "summary": "The authors fine tune a LLM to produces a structured description of a garment (panels, shapes, stitching)  using tokens learned from a Residual VQVAE.  They train it on a novel 1M sample dataset of garments - the dataset is constructed using modern image generators to make them appear more realistic.", "questions": "Can it handle the jean shorts shown in figure 3?  How does the segmentation error shown impact the data?\nWhich papers or frameworks support the residual quantization claim?  Which aspects of it are novel?\nHow are arcs compared—pointwise, via curvature metrics, or other geometry loss?\nWhat exactly is “commitment loss”? Why is it not described elsewhere?\nHow do authors verify “garment structure consistency”—visual inspection or metric-based?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Ambitious attempt to bridge SMPL renders and realistic imagery.\nIntroduces automatic data curation pipeline for garment datasets.\nLarge-scale dataset construction (1M+ samples) is potentially impactful.\nMentions residual quantization for compression–quality balance.", "weaknesses": "Abstract: “Paradigm shift” reads as add-speak / promotional, not scientific.\n\n057: RCQ-VAE insufficiently defined; unclear whether novel or existing concept. Missing citations for residual or hierarchical quantization methods. (significant)\n189: “Such as” vague; replace with “including … as shown in experiments.”  They used specifically both later.  (minor)\n200: “Ours” should be “Our” or omitted for clarity. (minor)\n253–254: Unclear comparison procedure for arcs; method not described (significant)\n253–254: Commitment loss mentioned without definition or formula. (this is more severe)\n280: SMPL acronym unexplained at first mention (minor)\n291: Claim of “ensuring garment structure consistency” unsubstantiated by evidence. (significant)\n298: “Real” oversells results; “realistic” or “photorealistic” more accurate. (easily addressable)\nFig. 3: Visible segmentation errors and unrealistic texture blending undermine claims.(somewhat significant - would help to show the same garments)", "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": "yGyfF5FaKE", "reviewer": "Reviewer_qZMb", "summary": "This paper proposes a novel framework called GarmentGPT for generating 2D sewing patterns from multi-modal inputs (such as images and text). GarmentGPT introduces a Residual Vector Quantizer Variational AutoEncoder (RVQ-VAE) to transform the pattern generation problem from a continuous coordinate regression task into a discrete, compositional sequence generation task. Furthermore, to address the scarcity of photo-to-pattern pairs for real-world applications , the paper also contributes a Data Curation Pipeline for synthesizing over one million \"photorealistic\" images paired with their corresponding \"GarmentCode\"", "questions": "1. The SOTA comparison (Table 2) is conducted on the GarmentCode Dataset , but the paper states the model was trained on the newly produced Real-Garments dataset (RealGarment-IM). Does this not create an unfair comparison? Please clarify the exact training set used for the SOTA comparison in Table 2 to ensure the comparison was fair.\n\n\n2. Given that the model was trained on the Real-Garments dataset, yet its performance on this benchmark shows a sharp decline compared to the structured dataset , can the authors analyze the main reasons for this performance drop? Does it stem from limitations in the model or in the data pipeline?\n\n3. Could you provide a qualitative or quantitative analysis of the main error types on the Real-Garments Benchmark? For example, what percentage of errors are high-level structural errors (e.g., incorrect number of panels) versus low-level geometric errors (e.g., inaccurate curve indices)?\n\n4. Inconsistency in Table 3: In the text of Section 5.3, the authors state the model used for the real-world benchmark is \"GarmentGPT (LLaVA-1.5 with Image+Text input)\". However, the caption for Table 3 reads \"Performance of GarmentGPT (Qwen-7B)\". Which model actually produced the results in Table 3?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper's greatest strength lies in its core concept. Instead of forcing VLMs to do what they are not good at (regressing thousands of floating-point coordinates) , the authors use an RVQ-VAE to create a geometric \"vocabulary\". The VLM then \"composes\" these geometric primitives, much like \"writing\" a sequence . This is an elegant and powerful solution.\n- The method achieves outstanding performance on the structured GarmentCode dataset, outperforming the regression-based SOTA model Alpparel by a large margin (e.g., +16.7% Panel Accuracy, +25.3% Stitch Accuracy).\n- The proposed Data Curation Pipeline , the synthesized 1M+ dataset (RealGarment-IM) , and the Real-Garments Benchmark  are important resources that can drive future research in this field.", "weaknesses": "1. The proposed \"Data Curation Pipeline\"  does not use real photos. Instead, it uses an image editing model (Qwen-image-edit) to convert rendered images into \"photorealistic\" images. This is a simulation process. The results in Table 3 show that the model's performance drops sharply when transferring from structured data (Table 2) to this new \"real\" benchmark (Table 3) (e.g., Panel Accuracy drops from 95.62% to 47.81%) . This indicates that the data pipeline fails to effectively bridge the sim-to-real gap. Therefore, the paper's claim of being the \"first [model] trained on large-scale real photos\"  is misleading.\n\n2. The paper reports poor performance on the Real-Garments Benchmark (Table 3)  but provides no in-depth analysis. Why is the performance degradation so significant? Is it a failure of the VLM's perception, or poor generalization of the RVQ-VAE tokens to real images? What are the most common error types (e.g., incorrect panel count? incorrect curve shapes?)? The lack of this analysis is a missed opportunity.\n\n3. Lack of clarity and fairness in the experimental comparison. The paper states in Section 5 \"Framework\" that the VLM was fine-tuned on \"our curated dataset\" , which is defined in Section 4 as the newly created RealGarment-IM dataset. However, the SOTA comparison experiment against baselines like Alpparel (Table 2) was conducted on the GarmentCode Dataset . This makes it difficult to determine whether the performance improvement comes from the data or the innovative model architecture.", "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)