bg-effect

Take a Peak

Digital Fashion — Clothes that aren’t there

Sitting in a cozy café in your favorite t-shirt, with one click you change into a shirt and put on a jacket. You can start a conference with your future client. Such perspective is becoming more and more real, and closer than ever, due to concept of Digital Fashion.

 

 

Pic. 1. Source

 

 

With the development of new technologies, especially 3D graphics (rendering, 3D models and fabric physics), the term is becoming increasingly popular. And what is Digital Fashion really? It is simply digital clothing – a virtual representation of clothing created using 3D software and then „superimposed” on a virtual human model.

 

 

Gif. 1. The Fabricant

 

 

Digital Fashion seems to be the next step in the development of the powerful e-commerce and fashion markets. Online stores started with descriptions and photos; now 360° product animations have become the norm, and digitally created models’ faces and bodies are increasingly being used for promotional graphics. The time for virtual fitting rooms and maybe even our own virtual wardrobes is coming. Actually, this (r)evolution has already taken its first steps. Let us just look at AR app projects of brands such as Nike (2019) or the collaboration of Italian fashion house Gucci with Snapchat (2020).

 

Gif. 2. Application for virtual shoe fitting. Source

 

Where did the need for this type of solution come from? The main, but not the only, factors giving rise to this type of application are:

 

On-line work and social relations – more and more events are moving or taking place simultaneously in the virtual world. The same applies to professions and even social gatherings. Remote working „via webcam” is no longer the domain of the IT industry, but increasingly appears in the entire sectors of the economy.

 

Environmental consciousness — digital clothes and accessories do not require farmland or animal husbandry for fabric and leather, as well as 93 billion cubic meters of water to produce textiles, laundry detergents, or global distribution routes. Designed once anywhere in the world, they can be globally available in no time.

 

The rapid increase in the popularity of items that do not exist in the real world – NFTs (non-fungible tokens) and people adopting digital alter egos.

 

The new generations are natives of technology. They largely communicate, and thus express themselves, in the virtual world. A perfect example of this trend is the success of fashion house Balenciaga’s campaign done in cooperation with the game Fortnite. Digital-to-Physical Partnerships will become more and more common.

 

Above, I have only outlined the emerging niche of Digital Fashion. It is also worth mentioning Polish achievements in this field – those interested may refer to the VOGUE article on the Nueno digital clothing brand and the article on homodigital.pl. Personally, I am extremely curious what virtual reality will bring to the e-commerce and fashion market in the coming years.

 

Pic.2. Digital Clothes made by STEPHY FUNG.

 

VR/DF Application — Big Picture

The rapid development of the Digital Fashion niche observed in recent years gives us huge, still largely undiscovered opportunities for the development of new products and services in this area. From designers specializing only in Digital Fashion, through professionals selecting textures for virtual fabrics, to programmers responsible for the unique physics of clothes. Personally, my favorite option would probably be to turn off gravity – you are sitting safely in a chair, and the shirt you’re wearing is acting like you’re in outer space. So naturally, space is created for apps that showcase emerging products and for marketplaces where customers will be able to view and purchase them.

 

For the purpose of this article, we will take on the challenge of creating just such a solution – an AR app connected to a digital clothing marketplace. The application will give the user the to create their own virtual styling, and clothing brands, as well as related brands, to officially sell their products and NFT.

 

Basic application principles

In theory, the operation is very simple – the application collects data about the user’s posture from the camera image, then processes it in real time using a library for human pose estimation (technology: OpenCV + Python). The collected data is actually just points in 3D space. They are transferred to the 3D engine, in which a virtual model of the User is created. The 3D model of the character itself is invisible, but interacts with visible clothes and/or accessories (technology: Blender 3D + Python). Ultimately, the user sees himself with the digital clothing superimposed.

 

Pic. 3. Diagram of the components of the application responsible for the virtual scene.

 

At this point, it is worth clarifying two terms:

 

 

POSE ESTIMATION — pose estimation is a computer vision technique that predicts movements and tracks the location of a person. We can also think of pose estimation as the problem of determining the position and orientation of a camera relative to an object. This is usually done by identifying, locating and tracking a number of key points on a person, such as the wrist, elbow or knee.

 

 

RIGGING(skeletal animation) means equipping a 3D model of a human, animal or other character with jointed limbs and virtual bones.These form a skeleton inside the model, which makes it much easier and more efficient for the animator to maneuver – movements of the bones affect the movement of the 3D model.

 

The exchange of information between the program making the pose estimation and the skeleton inside the human model is the basis of the created application. Data packets about the position of characteristic points on the body, which are x, y, z parameters in space, will be connected with the same points in rigging of the 3D model of the figure.

 

 

Pic. 4. Overlaying points from pose estimation on the joints of a 3D human model.

 

 

General guidelines for business objectives

The proposed solution does not go in the direction of a virtual avatar (i.e. it does not position itself as a replacement for a person’s image). We are interested in the environment around the person, in the surroundings – clothes, accessories, interiors, etc. – what is around is already a product. Following the proverb „closer to the body than the shirt”, the closest and always fashionable product are clothes – hence we will strongly focus on this segment of the market.

 

The question arises – what if the user wants to change their eye color? From there it’s close to swapping your hand for that of the Terminator after the fight in the final scene. I identify such needs as very interesting (e.g. in Messenger filters), but infantile. I would describe the proposed solution as a place of man + product, rather than man + visual modification of man. This is intended to imply an image of greater maturity, professionalism and brand awareness. In practice, it is meant to be a place where existing brands can sell products right away. The product focus is also meant to clearly differentiate this solution from the filters familiar from TikTok/Instagram, or animated emoticons on iOS.

 

Clothing in Metaverse

Just how fresh and hot the topic of digital clothing, and the entire emerging market associated with it is, is indicated by the huge interest generated by the Connect 2021 conference, during which the CEO of Facebook, or, for some, META, presented the Metaverse (’meta’- beyond, and 'universum’- world). This is the concept of a new internet combining the 'internet of things’ with the 'internet of people’. Mark Zuckerberg explained in an interview with The Verge that the Metaverse is „an embodied internet where instead of just viewing content – you are in it”. The author of the term itself is Neal Stephenson, who used it nearly thirty years ago in his cyberpunk book Snow Crash. In it, he describes the story of people living simultaneously in two realities – real and virtual.

 

The question is not „will it happen?” but rather „when and how it will happen?” As augmented, and virtual reality technologies become increasingly present in our lives, the world that now surrounds us on a daily basis will migrate into the Metaverse. Offices, pubs, gyms, flats are all now our mundane lives and will also be present in digital life. At the center, however, will always be people and their experiences. But what would interactions with others be like without the right attire? A „burning” t-shirt of your favorite band at a virtual concert; a waterfall dress during a New Year’s Eve meta-ball, or a golden shirt at a business meeting summarizing a successful project – although it sounds like science-fiction, this series of articles is an attempt to respond to such needs.

 

 

Gif.3. Digital clothing in Metaverse

 

Conclusion

The evolution of the e-commerce market towards Digital Fashion has already begun. This is possible thanks to the dynamic development of technologies such as Pose Estimation, 3D graphics, and hundreds of other smaller, but very important, innovations appearing every day. In this article, we’ve given an overview of what digital clothing is and the opportunities it presents – for software developers on the one hand, and designers and graphic designers on the other.

 

In the future articles we will focus on technical issues related to the created application and market. Those interested can count on a large dose of code in Python associated with Pose Estimation and Blender 3D. There will also be plenty of news related to Digital Fashion and Metaverse.

bg

Clothes that aren't there. AR and Python in the Digital Fashion.

Sitting in a cozy café in your favorite t-shirt, with one click you change into a shirt and put on a jacket.

Read more arrow

Introduction

AWS Glue is an arsenal of possibilities for data engineers to create ETL processes with Amazon resources. It supports a setup of calculating units where jobs can be in the form of Python or Spark scripts made from scratch or using AWS Glue Studio with an interactive visual designer. The designer has a simple interface and comes up with helpful set of ready to use transformations. Still, it also presents some limitation and problems.

The Limitations

The visual designer automatically generates a script for every added transformation. This script can be modified, however, any change to it will block the possibility for further visual development as user code cannot be translated into visual transformations.

 

Currently there are 15 available transformations, like Select Fields, Join, or Filter. Those basic operations cover up most of typical data operations, yet there is always a need for more complex calculations. In those situations, SQL and Custom transformations come to the rescue. First one extends the job’s capabilities only to SQL functions. Second one allows to create a new transformation with user made Python function that can only accept one parameter and always need to return DynamicFrameCollection.

 

If there is a need to extend a job with additional parameters they need to be added in the job’s configuration, yet they are also needed to be added manually to the script. If a developer builds the job with visual templates, it makes them impossible to do the development further in the visual designer, as a proper visual operation to add jobs’ parameters into script is not implemented.

 

The Problems 

Some transformations, like SelectFields, do not handle empty datasets in a proper manner. If empty dataset needs to be processed, those transformations will return an empty object without headers. This in turn will lead to an error in the next step, if any processing is applied on the indicated columns.

 

There are several problems with the web interface itself, i.e., a significant amount of used visual transformation leads to a complete slowdown of the designer, or if someone wants to change the data type for only one column in ApplyMapping with selection menu, this sometimes causes unexpected changes in all other columns.

 

Data preview is a great addition to AWS Glue Studio as it allows to observe how parts of data are processed through every transformation. However, if there is any error in a job, it prints a general error message and restarts itself to print the same message on and on. This does not allow to really validate the error, which sometimes forces you to stop viewing the Data preview and run the job in standard mode.

bg

AWS Glue– Tips for Beginners Part II. Limitation of AWS Glue Studio

AWS Glue is an arsenal of possibilities for data engineers to create ETL processes with Amazon resources.

Read more arrow

Introduction to Case Study

AWS Glue is, amongst other AWS services, a great choice for a Big Data project. Alone or even with other services, like AWS Step Function and AWS EventBridge, it may help create a fully operational system for data analysis and reporting. The service provides ETL functionalities, facilitates integration with different data sources and allows a flexible approach to development.

 

In the following paragraphs I present a review of AWS Glue features and its functionalities based on a real example of integration with external databases and loading data form there to S3 buckets. Whole purpose of this exercise is to present technical side of the service using a practical case and building a simple solution step by step.

The Connection

In the reviewed case, the data source is a PostgreSQL database which is an external resource from AWS. It stores few tabular datasets that are supposed to be moved to Amazon S3. Someone could create a connection to scan this database directly In a form of a script, but here we can use AWS Glue Connections. It allows to create a static connection to databases which stores connection’s definition, the chosen user and its password. It delivers a possibility to connect external databases, Amazon RDS, Amazon Redshift, MongoDB and others.

Crawlers

Based on the established connection in AWS Glue, it is possible to scan databases to know what tables are available there. Developers can use AWS Glue Crawlers which may analyse whole databases model for a chosen database schema to create an internal representation of tables. A Crawler can be run manually or based on a schedule to scan one or more data sources. A successful scan of Crawler creates metadata in Data Catalog for Databases and Tables.

Databases and Tables

Databases in AWS Glue serve a purpose of containers for inferred Tables. Tables are just metadata and they reference actual data in an external source, i.e., their data are not saved in Amazon storage. In a situation where inferred Tables are created with Crawler scanning internal Amazon resources, those Tables would also act only as references. This means that deleting Tables in AWS Glue would only lead to deletion of metadata in Data Catalog, but not to deletion of physical resources on external databases or S3. What developers must also remember is that Tables from external resources are not available for ad-hoc queries using Amazon Athena, even though scanned Databases exists in Amazon Athena.

The Jobs

AWS Glue lets developers create Spark or simple Python jobs, where jobs’ settings can be modified to select type of workers, number of workers, timeouts, concurrency, additional libraries, job parameters and so on. Developers may create a job by writing and passing scripts using Amazon platform or using recent feature in AWS Glue Studio to create jobs with a visual designer.

 

Picture presents a Glue Studio job in a visual form (left) and its representation in code (right).

 

 

Continuing with the case study, in the above picture there is a visually created job that would import data from PostgreSQL databases into S3 bucket. In this simple example, there are only three operations used (left side of the picture): Data source, Transform and Data target. Those operations and additional other built-in transformations simplify the process of creating Glue jobs. First operation directly creates a data frame from an external table by simply indicating Database and Table created in the previous steps. Then, by “filter” transformation, only specific data are saved into S3 bucket with the last operation.

 

All those three steps can be done manually just by the means of passing parameters in the visual designer. Moreover, visual transformations will generate a ready to run script (right side of the picture). This script can be modified, but that irreversibly switches off a possibility of further modification using the visual designer. This limitation only allows creation of simplest jobs or a start-up of bigger jobs.

 

The above steps show the features of AWS Glue. Some of them could be omitted, if one would like to create his/her own way of connecting to a different data source using credentials stored in AWS Secrets Manager instead of creating Connection in AWS Glue. Additionally, there are a couple more useful functions of AWS Glue that were omitted in this article, like Workflow, or Triggers. Apart from the nice sides of AWS Glue, there are some disadvantages that need to be taken into consideration. Those will me mentioned in next article about AWS Glue.

bg

AWS Glue – Tips for Beginners. Part I – Review of the Service

AWS Glue is, amongst other AWS services, a great choice for a Big Data project.

Read more arrow
Load more
vector