3-dimension (3-D) printing technology is growing strongly with many applications, one of which is the garment industry. The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics. This paper proposes a method to construct 3-D human models by applying deep learning. We calculate the location of the main slices of the human body, including the neck, chest, belly, buttocks, and the rings of the extremities, using pre-existing information. Then, on the positioning frame, we find the key points (fixed and unaltered) of these key slices and update these points to match the current parameters. To add points to a star slice, we use a deep learning model to mimic the form of the human body at that slice position. We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically. We combine all slices to construct a full 3-D representation of the human body.

Recently, applying 3-D printing [

Anthropometric surveys toward applications in the garment industry were conducted early by nations worldwide but were primarily performed by traditional hand measurement. Japan was the first country to undertake a large-scale countrywide survey using 3-D body scanners from 1992 to 1994. Other nations, in turn, performed anthropometric surveys and built their own body measuring systems utilizing 3-D body scanning technologies.

Many research groups have conducted building 3-D human models, from building a part of the human body (ears, human organs) to constructing the entire body to serve different needs such as health care, fashion, etc. Thanks to the quick growth of technology and skill, many methods of building 3-D human models have been improved. They are being applied in modern printing and scanning 3-D equipment to respond demands of building complete human models. Kieu et al. [

Point cloud learning has been gaining more attention recently due to its effectiveness in many applications such as computer vision, autonomous vehicles, and robotics. Deep learning has dramatically solved visual problems [

In this study, we propose a new method based on deep learning and point cloud learning to conduct training of 3-D models. First, we convert the 3-D model into point clouds: Get the uniform point per slice with a probability proportional to the surface area, using the built-in function in the pytorch3D library. This step converts the input models into a set of model point clouds to form a complete mesh. Then, we move the input cloud points such that all of these points come close to the hand measurement data. Finally, we construct a 3-D model of the person corresponding to the data. We repeat those steps until all the cloud points have the lowest error compared to the hand-measured data.

We train our method on the 3-D model set and the model surface. Contrary to previous approaches, which need a lot of data and need to calculate the interpolation in detail, our point cloud learning method has a lower error and better control over input data and data when training the model. Also, our model has improved how to train the model on the whole 3-D model.

3-D models are being applied in many types of industries. The health industry uses sophisticated agency models made of many slices of two-dimensional pictures from a Magnetic Resonance Imaging or Computed tomography scan to create vivid and realistic scenes. These models are used in video game business as assets for computers and video games. In recent decades, the Earth Science community has begun to use 3-D geological models as a common practice. Physical devices such as 3-D printers or Computer Numerical Control machines can also be formulated based on 3-D models [

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Deep learning is a form of machine learning that uses a collection of algorithms to decode high-level model data utilizing many processing layers with structural combinations or by integrating many additional nonlinear factors. Deep learning comprises several methods, each of which has its own set of applications to the problem [

− Deep Boltzmann Machines (DBM)

− Deep Belief Networks (DBN)

− Convolutional Neural Networks (CNN)

− Stacked Auto–Encoders

Although they may also incorporate propositional formulae or latent variables arranged by layers, most current deep learning models are based on artificial neural networks, especially CNN. Nodes in DBN and DBM are examples of deep growth models [

In deep learning, each level learns to transform the input data into a slightly more abstract and synthetic representation. In the application of image recognition, input is a matrix of pixels. The first layer of a CNN model encodes edges. The second layer can compose and encode the alignment of the edges. The third layer can encode the nose and eyes. The fourth layer can recognize that the image has a face. Deep learning can learn critical features to place at what level optimally [

An artificial neural network consists of a group of artificial neurons (nodes) connecting and processing information by passing connections and computing new values at the nodes (connectionism approach to computation). In many cases, an artificial neural network is an adaptive system that changes its structure based on external or internal information flowing through the network during the learning process.

To calculate errors of 3-D model, we use the method in [^{th}

^{th}

^{th}^{th}

Error of slice is calculated using

The error is calculated based on 3-D model after training and 3-D output model, using

To build a 3-D human model, we deform a 3-D input model to create a target model according to hand measurements. To turn the input model into a target (according to the problem requirements) in each iteration, we move point in the set of points of the model from the original input model in order to build the output model so that the points of the input model get closer to the corresponding point of the output model . After finishing iterations, the distance between the points on the input model and the corresponding points on the output model will be reduced.

We set up an Algorithm 1 to make the meshes structure and algorithm 2 to build the train model. Based on Algorithm 1 and Algorithm 2, we create a 3-D model of the human body by Algorithm 3. In this article, we use deep learning to transform the original model. We choose two input 3-D models to apply the proposed method, including the 3-D sphere model (3.1) and 3-D standard humanmodel (3.2).

The input model is first selected as a sphere model make up of 12 fixed points (

After using Algorithm 1, 2, we receive a shape of 3-D human model shows in

We see that the arms and the legs are sticky together, creating ‘membranes’ between them. We can remove these ‘membranes’, but it takes a lot of time and has a poor result because the shape will appear wrong when removing ‘membranes’.

Because the method of 3.1 produces a poor result, we change the input model. Instead of a sphere, we choose an input model of a 3-D standard human model. We choose the so-called standard input model from a set of 3-D human body models that are given. We define the standard model with _{i}: height of the i ^{th}model.

The process is identical to changing from a sphere to a person. However, instead of using a spherical model as the input mesh, we use a 3-D model of the human.

We trained the model using Python and Google Colab platform with graphics processing unit assistance to accelerate the training process. We executed the trained model on the personal computer with an Intel Core i5 7th Gen processor and 8GigaByte random access memory.

The initial data was gathered from two sources, emphasizing youthful and middle-aged persons of both sexes, including over 1000 human models (378 male models and more than 600 female models) provided by the garment research group at Hanoi University of Technology [

The errors acquired from experimenting with the equipment as mentioned above, with 12500 data points and 5000 data points, are shown in

Error | 12500 points | 5000 points |
---|---|---|

Average | 0.00026 | 0.000615 |

Max | 0.000322 | 0.000675 |

Min | 0.000232 | 0.000572 |

From

After training the 3-D model with 10000 iterations, we found that the error rate significantly decreased in the first 1000 iterations, then slightly reduced. The difference between loops is minimal from the 6000 iterations onwards. Based on

Slices | 12500 points | 5000 points |
---|---|---|

Neck | 0.4711 | 1.50532 |

Chest | 0.4966 | 2.2675 |

Stomach | 0.4689 | 1.493659 |

Buttocks | 0.5543 | 1.213283 |

Upper arm (left) | 0.7402 | 1.45723 |

Upper arm (right) | 0.8505 | 1.086267 |

Wrist (left) | 0.7366 | 1.404546 |

Wrist (right) | 0.7608 | 1.16059 |

Thigh (left) | 0.3234 | 0.9478933 |

Thigh (right) | 0.4167 | 0.9461028 |

Calf (left) | 0.556 | 1.496387 |

Calf (right) | 0.6215 | 1.442509 |

Ankle (left) | 0.3653 | 1.486258 |

Ankle (right) | 0.354 | 1.167027 |

Slices | Male | Female |
---|---|---|

Neck | 0.4711 | 0.39228 |

Chest | 0.4966 | 1.89187 |

Stomach | 0.4689 | 0.46008 |

Buttocks | 0.5543 | 0.73364 |

Upper arm (left) | 0.7402 | 0.5525 |

Upper arm (right) | 0.8505 | 0.61004 |

Wrist (left) | 0.7366 | 0.71498 |

Wrist (right) | 0.7608 | 0.7865 |

Thigh (left) | 0.3234 | 0.53612 |

Thigh (right) | 0.4167 | 0.40858 |

Calf (left) | 0.556 | 0.43845 |

Calf (right) | 0.6215 | 0.4083 |

Ankle (left) | 0.3653 | 0.48125 |

Ankle (right) | 0.354 | 0.37997 |

Moreover, as shown in

The more the number of data points, the more decrease in error, implying that the error rate is proportional to the number of points in the model. More data points lead to more comprehensive and precise results, increasing accuracy and lowering error.

To verify the effectiveness of the method compared with other methods,

Error | Interpolation [ |
CNN [ |
Proposed method | ||||
---|---|---|---|---|---|---|---|

Male | Female | Male | Female | ||||

5000 points | 125000 points | 5000 points | 125000 points | ||||

Average | 11.02621 | 0.09860 | 0.13160 | 0.00062 | 0.00027 | 0.00059 | 0.00026 |

Max | 125.55837 | — | — | 0.00068 | 0.00031 | 0.00069 | 0.00032 |

Min | 1.38504 | — | — | 0.00057 | 0.00023 | 0.00059 | 0.00023 |

The proposed method reduces the computational step since we train on the surface (known as the skin) and the entire model synchronously, without dividing into main slices and separate parts (arms, legs, body, etc.) like interpolation [

As a result, in practical applications, particularly in clothing, it is essential to have a reasonably realistic human model in terms of shape and size without needing to be extremely specific about each part and minute detail. Then, in terms of both time and accuracy, building a model with 5000 data points is a viable alternative.

Machine learning, especially deep learning, has been used to develop a method for creating a 3-D model of the human body. This technique takes less time to execute and has fewer errors than prior approaches. However, this technique has several drawbacks, including that the resulting model has a rough surface and needs to add a smoothing step. Besides, the resulting model makes errors about shape, especially the wrist. There are drawbacks that we could not fix in research time. We will develop automatic smoothing algorithms while training 3-D models and improve 3-D model algorithms with more than data points to reduce error in the shape of the wrist in the future.