# Generating Datasets of 3D Garments with Sewing Patterns

Maria Korosteleva
Graduate School of Culture Technology
KAIST
<EMAIL_ADDRESS>
&Sung-Hee Lee
Graduate School of Culture Technology
KAIST
<EMAIL_ADDRESS>

###### Abstract

Garments are ubiquitous in both real and many of the virtual worlds. They are
highly deformable objects, exhibit an immense variety of designs and shapes,
and yet, most garments are created from a set of regularly shaped flat pieces.
Exploration of garment structure presents a peculiar case for an object
structure estimation task and might prove useful for downstream tasks of
neural 3D garment modeling and reconstruction by providing strong prior on
garment shapes. To facilitate research in these directions, we propose a
method for generating large synthetic datasets of 3D garment designs and their
sewing patterns. Our method consists of a flexible description structure for
specifying parametric sewing pattern templates and the automatic generation
pipeline to produce garment 3D models with little-to-none manual intervention.
To add realism, the pipeline additionally creates corrupted versions of the
final meshes that imitate artifacts of 3D scanning.

With this pipeline, we created the first large-scale synthetic dataset of 3D
garment models with their sewing patterns [1]. The dataset contains more than
20000 garment design variations produced from 19 different base types. Seven
of these garment types are specifically designed to target evaluation of the
generalization across garment sewing pattern topologies.

Figure 1:  Example garments from our dataset of 3D Garments with Sewing
patterns. The dataset is obtained with our original garment generation
pipeline that samples novel designs in sewing patterns space.

##  1 Introduction

The task of learning-based object structure estimation from various input
modalities has seen significant development in recent years. Both
reconstruction and generative models that rely on structure tend to produce
more consistent and plausible results comparing to the models without explicit
structure modeling, as demonstrated by the recent successes in the area [2, 3,
4, 5]. Yet, most of the works in the domain rely on the datasets of rigid
objects, while datasets of structured deformable objects suitable for Deep
Learning applications are practically not available.

On the other hand, the growing areas of 3D garments reconstruction and
learning-based cloth simulation [6, 7, 8, 9, 10] do target deformable objects
but rarely exploit the fact that most garments have a well-established base
structure – a sewing pattern. The 3D garment data in these domains are scarce,
and no known datasets of significant size provide the garment sewing patterns
alongside the 3D garment models or renders. Another challenge is presented by
the variety of designs the garments exhibit in the real world, and existing
datasets only describe a fraction of the design space.

To address these issues, we propose a flexible generation pipeline that allows
dense sampling from the design space of garment sewing patterns. We assume a
sewing pattern to be a collection of the 2D pieces of fabric (panels) with a
known placement of each panel around the human body and information of how the
panels are stitched together to form the final garment. We model a panel to be
a closed piece-wise curve with every piece (edge) being either a straight line
or a Bezier spline. Such a sewing pattern is a close approximation of how most
real-world garments are designed.

The first step of our pipeline is the definition of a parametric sewing
pattern template. To streamline this task, we propose a JSON-based file format
to specify a base sewing pattern as a structure alongside a set of rules
describing allowed changes on the panels’ edges and the set of constraints on
edges to keep (Section 3.1). The proposed format allows efficient and uniform
descriptions of design spaces of garments that share the same sewing pattern
topology.

The rest of the pipeline is fully automatic. A dataset generator automatically
draws a requested number of random sewing patterns from provided template
specification, and then each of the generated sewing patterns is simulated on
top of a human body model to create draped 3D garment shape (Section 3.3). To
bridge the gap between clean simulated meshes and typical in-the-wild data,
each 3D garment can be additionally post-processed. This last step imitates
the artifacts of the typical 3D scanning process by removing parts of the
surface that are not visible from the outside view (e.g., occluded by folds),
as described in Section 3.4.

The datasets obtained with our pipeline have a well-controlled design
distribution, with the datapoints being well-aligned both in 3D space and in
sewing pattern spaces. Extending and creating new datasets comes down to
designing a new template which can be done as needed.

Using the designed pipeline, we created a large-scale dataset of garment 3D
models with thousands of garment design samples, coming from 12 template
garments that target model training and 7 templates to evaluate the
generalization across sewing pattern topologies (Section 4). We hope that the
community will help expand the dataset of garment designs even further by
creating new garment templates. The dataset, along with the base templates, is
available on Zenodo [1]. Code for the generator is available on
GitHub111https://github.com/maria-korosteleva/Garment-Pattern-Generator.

##  2 Related Work

This section gives a brief overview of the existing datasets related to 3D
garments.

Few publicly available datasets provide garment patterns alongside 3D garment
models. Berkeley Garment library [11, 12] provide sewing patterns geometry,
but consists of only 21 garment examples of different garment type and thus
not suitable for training Deep Learning models that aim to generalize across
garment designs as in our work. Wang et al. [13] released part of their
training dataset publicly, and it contains both 3D models and garments but
only offers one garment type (sweaters) with a simplified pattern topology
that consists of a single panel. Vidaurre et al. [14] describe a dataset of 19
garment examples with corresponding sewing patterns, all of them sharing the
same pattern topology. Our data generation pipeline allows obtaining large-
scale datasets suitable for training Deep Learning models and covering various
garment types and designs, as demonstrated by our dataset.

Other notable datasets of 3D garments that do not provide sewing pattern
information include datasets constructed from real scans, such as BUFF [15],
Multi-Garment Net [6], CAPE [7], Deep Fashion3D [16], and SIZER [17], and
synthetic datasets, such as 3DPeople [18], BCNET [19], and CLOTH3D [20]. In
terms of design variation, Deep Fashion3D [16] is the largest among the ones
based on real data featuring the current state-of-the-art 563 diverse scanned
clothing items. However, it is still a rather sparse sample of the garment
distribution and hence does not eliminate the need for obtaining design
variations synthetically. As for synthetic datasets, 3DPeople [18] uses unique
outfits for each of 80 human characters, BCNET [19] rely on variations of six
base garment types (short and long templates of upper garment, pants, and
skirts), while CLOTH3D [20] relies on shape variations and combinations of
four template garments (t-shirt, top, trousers, and skirt). Our generation
pipeline gives means for even larger design diversity by introducing template
definition language. Our dataset relies on 19 base types, but most
importantly, it could be extended to include new base garments within the same
framework without additional effort in development.

To the best of our knowledge, none of the existing synthetic garment datasets
address the gap between clean artificial meshes and the noisy geometry of in-
the-wild scan data. We provide an imitation of some of the artifacts met in
the raw data to assist with bridging this gap in the downstream tasks.

As for the data generation process, our work shares the approaches of [13, 14,
19] that sample datapoints from a parametric sewing pattern and then drape
them on the target body. However, we take the idea further by creating a
language for defining parametric templates to simplify the generation of new
synthetic datasets of garment designs and expand the design spaces. CLOTH3D
[20] is one of the closest works to ours as it provides not only a synthetic
dataset but also a data generation method to vary garment designs
automatically. However, these design variations are introduced in 3D by
cutting and re-arranging the simulated garment models, which allows fast
operations but does not guarantee physically correct garment drapes. In
contrast, our work introduces variations in sewing pattern space before
applying physics simulation, hence providing realistic shapes.

##  3 Dataset Generation System

Figure 2: Example data instance. Our data generation pipeline samples the
design space of the base sewing pattern and drapes the sample over a body to
get the corresponding 3D model. We also generate a segmentation map of this
model (different colors correspond to distinct panels) and an optional version
of the 3D model that imitates 3D scanning artifacts.

This section presents our dataset generation pipeline. In order to make the
dataset generation easily extensible, our system follows the classical
software design principle of Separation of Concerns (SoC), in our case
separating the definition of parametric sewing pattern templates from garment-
type agnostic dataset construction. This approach means that creating a
dataset for a new garment type only requires the specification of a new
template without a need to change the system itself.

For a convenient and flexible definition of sewing pattern parametric
templates, we present a domain-specific file format (Section 3.1). The manual
template development process is further supported by a GUI (Figure 6), where
one can visualize the defined model and try out the behavior of a template
through all stages of the dataset generation.

The automatic dataset construction involves template sampling (Section 3.2),
draping the sewing pattern samples on provided body model (Section 3.3),
producing 3D mesh and its segmentation into pattern panels, and scan imitation
process that removes parts of the hidden surface when viewed from outside
(Section 3.4). The computational performance of our automatic stage depends on
many factors, from hardware setup to simulation configuration and garment
complexity, but we report the values for our dataset in Section 4 to provide a
reference.

Note that in this work, we are specifically interested in exploring designs of
garment shapes, and thus the pipeline is not currently optimized for automatic
sampling of material properties, body shape, or pose variations. These
parameters can be set freely before the generation process but remain fixed
thereafter.

The code of the data generator will be available for free for research and
other non-commercial purposes.

###  3.1 Pattern Template Specification

A sewing pattern template specification is a JSON-based file format with a
pre-defined domain-specific structure. A specification consists of the base
sewing pattern, parametrization, and, optionally, constraints to be held while
sampling parameter space. We chose JSON as a basis for our specification
because it is simple, human-readable and editable, and structured while still
convenient for automatic processing. Example template specifications are
provided with the dataset that accompanies this paper (see Section 4 for
dataset description).

####  3.1.1 Base pattern definition

Figure 3: Panel representation. The edges form an oriented loop and are
labeled sequentially. $(C_{x},C_{y})$ define the location of quadratic Bezier
control point for the curvy edge edge_1, expressed relative to the edge.

Figure 4: Parameter rule to control pattern length. It acts on the edges
length (hence the type "length"). The change is uniformly applied to both
panels on their side edges ("influence_list"). The direction of application
instructs to extend towards the lower part of the garment. The current value
of 1.5 shows that the edges are to be extended to 1.5 times their base length
(New shape is indicated as a lighter area on the pattern).

The first part of the template specification describes a base sewing pattern
that would then be parametrized and varied to produce new designs. The
structure for the pattern description has been designed to reflect the general
definition of a garment sewing pattern as closely as possible to allow a
variety of garments to be described with it, even those that we may not
foresee. Nonetheless, we added a simplifying restriction that the edge
curvatures need to be either linear or quadratic at this stage of development.
We also require edges to be oriented and defined sequentially to form a loop
(see an example in Figure 4, which is a rather natural way of describing
closed polygons. This requirement gives useful assumptions to rely on in
parameter rules specification (see Section 3.1.2).

Hence, the structure for specifying a base sewing pattern consists of the
following components:

  * •

Unordered list of panels. Every panel is, in its turn, a multi-component
structure described with the following elements:

    * –

A list of 2D coordinates of panel vertices, specified in local space of the
panel. The order can be defined arbitrarily.

    * –

An ordered list of oriented edges that form a loop. Each edge is described by
the IDs of two vertices it connects and 2D coordinates of the quadratic Bezier
curve control point (named curvature coordinates) if the edge is not a
straight line. The coordinates are specified relative to the edge (Figure 4).

    * –

Global translation and rotation (Euler angles) that define panel location in
3D around a particular body model, which would then be used for draping.

  * •

Unordered list of stitches. Every stitch is defined as a pair of edges to be
joined. Edges are references by the panel name and edge ID in the definition
of that panel.

####  3.1.2 Parameter rules

Figure 5: Parametrization for two of our base sewing patterns. The pants
template has three parameters defined, while a more complicated T-shirt
template defines nine parameters (Note that the sleeve parameters are applied
equally to all sleeve panels).

Our goal with pattern parametrization was to create a rule definition language
that is expressive enough to represent a variety of design variations,
allowing symmetric and asymmetric changes, garment parts varying
simultaneously (e.g. left and right sleeves) or independently from each other,
multiple variations to the same base pattern or even to the same area of the
base garment. On the other hand, we want the rule language to be structured
simply and to avoid heuristic assumptions on pattern topologies, such as
expecting certain components to be present or absent.

With these considerations in mind, we devised an approach to defining rules at
the panel edge level. An advantage of using such a basic building block is
that the scope of its possible variations is concisely defined comparing to
higher-level concepts like panels. Changes of length and curvature of
individual edges can be composed into semantically meaningful design
variations (e.g., extending sleeve length is equivalent to extending the edges
located alongside the arms). Defining rules for different parts of garments is
simplified to picking the set of edges from the panels of corresponding
semantic parts. Additional flexibility is achieved by controlling the way an
edge property varies (e.g., toward the start or the end of the edge, along the
edge itself or some other 2D vector).

Varying the panel vertex positions directly was another option to consider,
but it has some limitations. Vertices themselves do not hold information of
edge curvatures, which prevents the direct control of edge curvatures. Direct
access to edges directions is also highly useful as many variations are
naturally defined along edges in the garment sewing patterns.

The final proposed structure of parameter rule is composed of the following
attributes.

  * •

List of edges from any of panels that a particular parameter influences. These
edges are modified uniformly upon parameter sampling to enable symmetric
changes when needed.

  * •

Target property – curvature coordinates or edge length. Changing curvature
coordinates by parameter value is straightforward as it is a local property of
an edge. The edge length parameters, by default, create changes along the
edge’s main direction by modifying the edge’s 2D vertex positions with respect
to the local space of the panel. To allow more flexibility, changes can be
constrained to occur along an additionally specified 2D vector, e.g., only
vertically or to be distributed across a sequence of contiguous edges.

  * •

Parameter type – multiplicative or additive. The target property is changed by
multiplication by or addition with the parameter value.

  * •

Allowed range of values. Upon sampling, a new parameter value is drawn from
this range.

  * •

Current parameter value. For templates, it defaults to 1 in multiplicative and
0 in additive parameters.

An example definition of a parameter rule is given in Figure 4. Figure 5 shows
examples of complete sets of parameters for two base garments.

The order of applying multiple parametric rules may influence the result, and
thus the specification includes explicit definition of parameter order to
ensure sampling consistency and predictable results.

####  3.1.3 Constraints

In addition to the parameter rules, the template allows the definition of
constraints. Constraints are the rules that help restore desired consistency
of the sewing pattern stitches by forcing the edges on both sides of the
stitch to have the same shape. Thus, regardless of how the template parameters
are defined, the constrained edges will change together. The need for defining
constraints depends on base sewing pattern shape, parameter deviation, and
desired outcome. When preparing our dataset (Section 4), we found that for
many examples, it was possible to define parameters in the way that stitch
consistency is never broken even without constraints, except for one of the
skirts templates (Skirt 4 panels).

A constraint is specified as a list of affected edges and a per-edge
modification value (defaults to 1 in templates). Upon application, constraint
evaluates the average length of the specified edges and clamps or extends
every edge to this average length. Per-edge modification values store the
amount of change applied as a multiplier of the edge length, allowing to
restore the original state if needed.

###  3.2 Sampling sewing patterns

Figure 6: Our GarmentViewer GUI for testing sewing pattern templates
(developed within Autodesk Maya framework). It supports loading a pattern as a
mesh, changing parameters, testing simulation, segmentation, and scan
imitation on the loaded garment, and saving the current garment state.

With a pattern template defined, the next step is to sample individual
patterns from the template. This process is relatively straightforward. The
predefined parameters are applied to the base pattern one by one in the
specified order: a random value sampled from the specified range is applied to
the list of edges according to the parameter and property types. Afterward, if
any are defined, a list of constraints is applied to a sample. With the
flexibility of our parameter rules, some of the samples may contain
topological errors, specifically self-intersecting panels. Thus, every panel
of the newly generated pattern is examined for self-intersections, and if
detected, the pattern is discarded, and a new sample is drawn instead. The
final sample is saved in a JSON file with the same structure as the original
template but with updated sewing pattern definition and parameter values.

In principle, the choice of the panel order within a sewing pattern, and the
choice of the first edge in an edge loop of panels, can be arbitrary for
defining a sewing pattern shape. However, ensuring predictability and
consistency of these properties across the data samples can be beneficial on
the application side and would be hard to obtain if not introduced during
generation. Hence, we first pre-process the base pattern defined in the
template: the panels are sorted according to their 3D translations, in the
order of $X$, $Y$, and $Z$ coordinates; all edge loops are ensured to follow
counterclockwise direction; for every panel, we pick an edge that originates
from the lowest-leftmost vertex as the first on in the edge list. This
ordering is then propagated to all the samples from this template. Picking the
order by rules rather than using an arbitrary order made by a template creator
gives better consistency across different templates and, as a result, in
datasets constructed from multiple templates.

###  3.3 Obtaining garment 3D model

After sewing pattern samples are obtained, each is draped on top of a human
body model using physics simulation. Generally speaking, any 3D object can be
used as a body model, but it is recommended to use the same body model that
the template panels were placed around (see Section 3.1.1 for more on
placement) to increase the simulation stability. Material properties for both
fabric and body model and the desired mesh resolution can be set freely for
every sample batch. Final models are saved as OBJ files. In our work, we
choose Qualoth as the base physics simulator thanks to its robust production
quality results and the ability to work with garment sewing patterns as curve
collections, saving us development time.

The final 3D models are accompanied by per-vertex segmentation labels
indicating the panel each vertex belongs to or whether it belongs to the
stitch (see example visualization in Figure 2). These values are propagated
from an initial sewing pattern through the simulation process. Segmentation
labels are stored as a list in the TXT file.

Every simulated garment mesh is post-processed to ensure the high quality of
the output results. With high flexibility in defining design spaces, some
sewing pattern samples may produce 3D shapes that penetrate the body model or
have self-intersections. If these problems are not resolved during physics
simulation, the problematic sample is marked as failure, so that the sample
can be easily excluded from consideration in downstream tasks.

It is also important to note that the meshes produced in this step do not
share the same mesh topology but are well-aligned in 3D in terms of scaling
and global placement.

###  3.4 Imitating 3D scanning artifacts

The garment meshes produced by the simulation step are clean and describe the
garment shape completely, which is very much unlike the data captured in-the-
wild by 3D scanning or other means. To assist the downstream tasks with
generalization to such data, we add a post-processing step to imitate mesh
imperfections of the wild geometry. More specifically, we observe that most of
the means of obtaining 3D models would miss the regions not visible from
outside cameras, e.g., occluded by the garment’s own folds or body parts. We
imitate such artifacts on the 3D models produced by the simulation step, as in
the example shown in Figure 2.

To find occluded areas on an arbitrary garment, a draped garment 3D model and
a body model are placed in a tight box with an empty floor and ceiling,
representing a 3D scanner. Every face of a garment mesh is tested on
visibility from the walls of this box. The visibility test is performed by
simply shooting a bunch of rays in random directions from the center of a face
and checking if a given fraction of rays hit the walls. The invisible faces
are removed, as well as any vertices that have all of their adjacent faces
removed. The fraction of visible rays can be specified during data generation,
but we found that the value around 10% produces reasonable results. The
segmentation labels of the original mesh are also propagated to the corrupted
meshes.

Other types of data acquisition artifacts, such as irregular mesh topologies,
vertex position noise, or missing parts due to a limited number of cameras,
are not included in the pipeline. We believe that these effects depend on the
task at hand and would be fairly easy to imitate as needed.

##  4 Our Dataset

Figure 7: Three of 19 templates that were used for generating our dataset.
Each template is represented as the base sewing pattern and its 3D model (in
the middle of each group), and two extreme variations of the base – with most
parameters set to minimum values (left) and most parameters set to maximum
values (right). Visualizations of all 19 templates are provided in Figures A1
and A2 in Appendix.  Figure 8: Example of a base garment from the test group
composed from the same panels as base garments of the training group but
arranged into a novel sewing pattern topology. This example evaluates the
capability to reconfigure a t-shirt into having a hood similarly to the open
jacket examples.

With the generation pipeline presented in Section 3, we created a large-scale
dataset of 3D garments with sewing patterns, which is made publicly available
under CC BY 4.0 on Zenodo [1]. The complete documentation is provided as
Datasheet [21] that accompanies the dataset, and this section presents an
overview.

The dataset contains a total of 23500 garment design samples, among which
22547 passed the simulation quality checks as described in Section 3.3 and
thus are safe to use in the downstream applications. To create this dataset,
we designed 19 sewing pattern templates, examples of which are given in Figure
7. The data samples can also be found in Figure 1 and throughout the paper.

The 19 garment templates are divided into training and test groups. The
training group of 12 templates aims to cover design spaces of typical simple
garments, including skirts, dresses, tops, pants, jackets, hoodies, and
jumpsuits, and reflect some topological variations among them. We draw 1000 to
2700 samples from each training template depending on the complexity of the
template parametrization. The number of parameters in the templates varies
from just 3 (e.g., in pants) to 11 (in a sleeveless dress). Seven templates
from the test group target the evaluation of generalization across sewing
pattern topologies. Base patterns are designed strictly from the same parts as
the garments from the training group, with the same parametrization, but these
parts are re-arranged to give new topologies that are not directly included in
the training group, as illustrated in the example in Figure 8. We draw 150
samples from each template of the test group. Note that the primary goal of
the suggested training/test division is to prevent some garment types from
being used during training and only appear at evaluation time. In practice, we
recommend including some portion of samples from 12 training templates into
the test set for an unbiased evaluation.

The template design and simulation process require a choice of body model and
material properties. We use an average female body model provided by SMPL [22]
in T-pose and a fixed set of material properties for all of the templates and
data samples we created. All of the garment samples have both clean meshes and
their versions with scanning artifacts.

###  4.1 Computation performance

The time required to design the template, the manual part of the work, ranges
from few hours to few days depending on the complexity of the base garment and
its parametrization, whether parts of other templates could be reused, and the
familiarity with it the process.

Compute time for the automatic stage of the dataset creation varied depending
on the hardware, simulation settings, and garment mesh vertex count. The
sewing pattern sampling stage is the least expensive, with about 1300 designs
generated per minute. For each garment, it took about 3 minutes for
simulation, 45 seconds for the scan imitation, and 1 minute for creating two
rendered images for visualization. The total time to produce the dataset would
be 47 days for physics simulation, 12 days for scan imitation, and 15 days for
rendering if all garment samples were sequentially processed. Parallel
processing is possible for the dataset generation and reduces the time
significantly. Reported values were obtained on machines with either Intel
Core i5-8500 CPU or AMD Ryzen 9 3950X CPU and a single NVIDIA Titan XP GPU
used by the renderer.

###  4.2 Potential usages of the dataset

We provide a highly consistent dataset that specifically targets garment
design variations. The dataset presents several challenges for the Deep
Learning research, including the following:

  * •

Modeling complex structures like sewing patterns, which are essentially sets
of variable length consisting of variable structured parts (panels) with
additional cross-references among panels’ components (as stitches are defined
on edges);

  * •

Understanding the construction principles of these sewing patterns and
generalizing across topologies;

  * •

Working with structured deformable objects.

These challenges would arise in both the cases when sewing patterns are used
as inputs (e.g., generating draped shapes) or outputs (e.g., estimating
garment structure from meshes).

##  5 Discussion and Future Work

This paper introduces a data generation pipeline with a flexible method to
define parametric sewing pattern templates, followed by an automated pipeline
of template sampling, batch simulation, and scanning artifacts imitation
modules. The paper is accompanied by a large-scale dataset [1] of more than
20000 garment designs with sewing patterns and draped 3D garment meshes. The
dataset was synthetically generated from 19 originally designed garment
templates.

##### Limitations

There is still much to be done to create datasets representing various garment
designs and diverse ways people wear the garments. The proposed data
generation pipeline is largely simplified by fixing material properties, body
pose and shape, and omitting fine features of sewing pattern design, such as
darts, pleats, or complex edge curves. We plan to enrich the current pipeline
to cover these variations and provide a broader range of base parametric
templates. On top of that, it would be interesting to reflect complex garment
arrangements, such as layering of fabric (e.g., ballroom skirts), use of
accessories, overlaying of multiple garments, and utilization of complex
materials like thick and bumpy winter coats.

##### Ethical concerns

We do not see significant risks of security threats or human rights violations
in our work or its potential applications. However, we do foresee that our
work contributes to the field of garment analysis and virtual garments
development fields overall. These efforts might eventually remodel the
clothing production, retail, and virtual garments development, leading to
changes in the workforce structure. Hence, there is a general concern that
some jobs might be automated fully or significantly to reduce the demand for
human workers, and the industries would need to act proactively to avoid the
social impact of such changes. Another issue that concerns us is the potential
for intellectual rights violations. Models build to evaluate garment structure
might be misused to reverse-engineer original works of garment design,
simplifying their unauthorized reproduction. The current regulations in this
area might need to be modified to include such technological advances.

## Acknowledgments and Disclosure of Funding

We would like to thank the anonymous reviewers for their helpful suggestions.
This work was supported by National Research Foundation, Korea
(NRF-2020R1A2C2011541), and Korea Creative Content Agency, Korea
(R2020040211). Autodesk and FXGear provided research licenses to their
software packages, Autodesk Maya and Qualoth, which helped us greatly speed up
the development of our pipeline. The members of Geomteam of Motion Computing
Lab, KAIST, deserve our deepest appreciation for the invaluable discussions
and moral support throughout this project.

## References

  * [1] Maria Korosteleva and Sung-Hee Lee.  Dataset of 3d garments with sewing patterns.  Zenodo. doi:10.5281/zenodo.5267549, URL: https://doi.org/10.5281/zenodo.5267549, June 2021.
  * [2] Siddhartha Chaudhuri, Iit Bombay ERSIN YUMER, Adobe Research HAO ZHANG, Jun Li, Kai Xu, Ersin Yumer, and Hao Zhang.  GRASS: Generative Recursive Autoencoders for Shape Struc-tures.  ACM Transactions on Graphics (TOG), 36(4):1–14, 2017.
  * [3] Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, and Hui Huang.  SagNet: Structure-aware generative network for 3D-shape modeling.  ACM Transactions on Graphics, 38(4):1–14, 7 2019.
  * [4] Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, and Leonidas J. Guibas.  StructureNet: Hierarchical Graph Networks for 3D Shape Generation.  ACM Transactions on Graphics (TOG), 38(6):1–19, 8 2019.
  * [5] Kaichun Mo, He Wang, Xinchen Yan, and Leonidas J. Guibas.  PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12351 LNCS:683–701, 3 2020.
  * [6] Bharat Lal Bhatnagar, Garvita Tiwari, Christian Theobalt, and Gerard Pons-Moll.  Multi-Garment Net: Learning to Dress 3D People from Images.  In The IEEE International Conference on Computer Vision (ICCV), pages 5420–5430, Seoul, Republic of Korea, 2019. IEEE.
  * [7] Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, and Michael J Black.  Learning to Dress 3D People in Generative Clothing.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6469–6478, Virtual, 2020. IEEE.
  * [8] Igor Santesteban, Miguel A. Otaduy, and Dan Casas.  Learning-Based Animation of Clothing for Virtual Try-On.  Computer Graphics Forum, 38(2):355–366, 5 2019.
  * [9] Chaitanya Patel, Zhouyingcheng Liao, and Gerard Pons-Moll.  TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style.  In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7365–7375, Virtual, 3 2020. IEEE.
  * [10] Donglai Xiang, Fabian Prada, Chenglei Wu, and Jessica Hodgins.  MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video.  In Proceedings - 2020 International Conference on 3D Vision, 3DV 2020, pages 322–332, 2020.
  * [11] Huamin Wang, James F. O’Brien, and Ravi Ramamoorthi.  Data-Driven Elastic Models for Cloth: Modeling and Measurement.  ACM Transactions on Graphics, 30(4):1–12, 7 2011.
  * [12] Rahul Narain, Armin Samii, and James F. O’Brien.  Adaptive anisotropic remeshing for cloth simulation.  In ACM Transactions on Graphics, volume 31, 11 2012.
  * [13] Tuanfeng Y. Wang, Duygu Ceylan, Jovan Popović, and Niloy J. Mitra.  Learning a shared shape space for multimodal garment design.  ACM Transactions on Graphics, 37(6):1–13, 12 2018.
  * [14] Raquel Vidaurre, Igor Santesteban, Elena Garces, and Dan Casas.  Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On.  Computer Graphics Forum, 39(8):145–156, 9 2020.
  * [15] Chao Zhang, Sergi Pujades, Michael Black, and Gerard Pons-Moll.  Detailed, accurate, human shape estimation from clothed 3D scan sequences.  In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, volume 2017-Janua, pages 5484–5493, Honolulu, Hawaii, USA, 2017. IEEE.
  * [16] Heming Zhu, Yu Cao, Hang Jin, Weikai Chen, Dong Du, Zhangye Wang, Shuguang Cui, and Xiaoguang Han.  Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images, 3 2020.
  * [17] Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung, and Gerard Pons-Moll.  SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing, 7 2020.
  * [18] A Pumarola, J Sanchez, G P T Choi, A Sanfeliu, and F Moreno-Noguer.  3DPeople: Modeling the Geometry of Dressed Humans.  In Proceedings of the IEEE International Conference on Computer Vision, pages 2242–2251, Seoul, Republic of Korea, 2019. IEEE.
  * [19] Boyi Jiang, Juyong Zhang, Yang Hong, Jinhao Luo, Ligang Liu, and Hujun Bao.  BCNet: Learning Body and Cloth Shape from a Single Image.  In Computer Vision – ECCV 2020, pages 18–35, Cham, 8 2020. Springer International Publishing.
  * [20] Hugo Bertiche, Meysam Madadi, and Sergio Escalera.  CLOTH3D: Clothed 3D Humans.  In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, European Conference on Computer Vision, pages 344–359, Virtual, 12 2020. Springer International Publishing.
  * [21] Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé, and Kate Crawford.  Datasheets for Datasets.  arXiv preprint, 3 2018.
  * [22] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-moll, and Michael J Black.  SMPL : A Skinned Multi-Person Linear Model.  ACM transactions on graphics (TOG), 34(6):1–16, 2015.

## Checklist

  1. 1.

For all authors…

    1. (a)

Do the main claims made in the abstract and introduction accurately reflect
the paper’s contributions and scope? [Yes]

    2. (b)

Did you describe the limitations of your work? [Yes] See Section 5

    3. (c)

Did you discuss any potential negative societal impacts of your work? [Yes]
See Section 5

    4. (d)

Have you read the ethics review guidelines and ensured that your paper
conforms to them? [Yes]

  2. 2.

If you are including theoretical results…

    1. (a)

Did you state the full set of assumptions of all theoretical results? [N/A]

    2. (b)

Did you include complete proofs of all theoretical results? [N/A]

  3. 3.

If you ran experiments (e.g. for benchmarks)…

    1. (a)

Did you include the code, data, and instructions needed to reproduce the main
experimental results (either in the supplemental material or as a URL)? [N/A]

    2. (b)

Did you specify all the training details (e.g., data splits, hyperparameters,
how they were chosen)? [N/A]

    3. (c)

Did you report error bars (e.g., with respect to the random seed after running
experiments multiple times)? [N/A]

    4. (d)

Did you include the total amount of compute and the type of resources used
(e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]

  4. 4.

If you are using existing assets (e.g., code, data, models) or
curating/releasing new assets…

    1. (a)

If your work uses existing assets, did you cite the creators? [Yes] Our work
uses SMPL body model as appropriately mentioned in Section 4

    2. (b)

Did you mention the license of the assets? [Yes] Section 3 mentions license
for code, and Section 4 describes license for the dataset.

    3. (c)

Did you include any new assets either in the supplemental material or as a
URL? [Yes] We are providing data generation code and our garment dataset as
URL for reviewers. The paper text will include references to the assets upon
publication.

    4. (d)

Did you discuss whether and how consent was obtained from people whose data
you’re using/curating? [N/A] Our data does not relate to people

    5. (e)

Did you discuss whether the data you are using/curating contains personally
identifiable information or offensive content? [N/A] Our data is synthetic,
hence the question is not entirely applicable. To be thorough, we give a note
on these matters in the datasheet that accompanies our dataset.

  5. 5.

If you used crowdsourcing or conducted research with human subjects…

    1. (a)

Did you include the full text of instructions given to participants and
screenshots, if applicable? [N/A]

    2. (b)

Did you describe any potential participant risks, with links to Institutional
Review Board (IRB) approvals, if applicable? [N/A]

    3. (c)

Did you include the estimated hourly wage paid to participants and the total
amount spent on participant compensation? [N/A]

##  Appendix A Appendix

###  A.1 Sewing Pattern Templates of the Dataset

This section presents the visualizations of all garment templates used to
generate our dataset. Figures A1 and A2 demonstrate templates that compose the
training and the test groups. To give the idea of the design distributions
encoded by each template, we present samples at the extremes of the design
distribution, which show the garment elements that are changing and the ranges
of these changes. Note that parameters are independent of each other. Hence,
samples in the dataset may exhibit any combinations of parameter values within
defined ranges.

Figure A1: A total of seven garment templates of the test group. Presented as
a base garment design (middle of each group) and its extreme variations – with
most parameters set to minimum values (left) and most parameters set to
maximum values (right). Figure A2: A total of 12 garment templates of the
training group. Presented as a base garment design (middle of each group) and
its extreme variations – with most parameters set to minimum values (left) and
most parameters set to maximum values (right).