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Paris Fashion Week 2023: Digital Artist Creates Fascinating Louis Vuitton Collection Using Artificial Intelligence ( AI)

YEET MAGAZINE

Jun 26, 2023 • 4 min read

Digital Artist Creates Fascinating Louis Vuitton Collection Using Artificial Intelligence ( AI)

Digital artist and developer Patrick Akasha managed to generate by computer and artificial intelligence a very futuristic collection of Louis Vuitton menswear , bags and streetwear in breathtaking realism...Read full article
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‌AI-generated art

By YEET MAGAZINE | Updated 0339 GMT (1239 HKT) July 07, 2023

‌If you're looking for specific methods to generate artificial intelligence drawing, these key concepts that can help you find relevant information and resources.

Patrick Akasha brings futuristic fashion to life with Artificial Intelligence

Specialized in 3D and computer graphics, this Moroccan YouTuber has just achieved the unthinkable by developing software capable of reimagining fashion collections of the most popular brands using canvas models.

Artificial intelligence (AI) has made significant progress in various domains, including the field of art and creativity. One fascinating application of AI is generating drawings and artwork. There are several techniques and algorithms used in AI to create drawings. Here are a few examples:

Generative Adversarial Networks (GANs):

GANs consist of two neural networks, a generator and a discriminator, that work together to generate realistic and visually appealing images. By training the generator on a large dataset of drawings or artworks, it can learn to generate new and original drawings.

Variational Autoencoders (VAEs):

VAEs are another type of generative model that can be used to create drawings. These models learn the underlying distribution of a given dataset and generate new samples by sampling from that learned distribution. By training a VAE on a dataset of drawings, it can generate new and diverse drawings based on the learned patterns.

Neural Style Transfer:

Neural style transfer is a technique that combines the style of one image with the content of another. By using convolutional neural networks (CNNs), the algorithm can extract the style and content features from two different images and generate a new image that incorporates the style of one and the content of the other. This technique can be applied to drawings as well, allowing AI to generate unique artistic styles.

Conditional Variational Autoencoders (CVAEs):

CVAEs are similar to VAEs but include additional conditioning variables. These variables can be used to control specific attributes or features of the generated drawings. For example, by conditioning a CVAE on the stroke width or color palette, it can generate drawings with desired characteristics.

These are just a few examples of AI techniques used for generating drawings. AI can be trained on various datasets, including sketches, paintings, or any other form of visual art, to create new and unique artwork. Keep in mind that while AI can produce impressive results, the creative aspect and originality of the drawings still heavily rely on the training data and the algorithm used.

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