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jcmarchi · 3 months

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Mirror AI: From technical breakthroughs to market trends in virtual try-ons for beauty brands

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Mirror AI: From technical breakthroughs to market trends in virtual try-ons for beauty brands

The onset of artificial intelligence in the cosmetics and skincare industry is revolutionizing traditional approaches to marketing, design, and consumer interaction through personalized virtual try-ons.

Virtual try-on technology allows consumers to sample different items online, without leaving the comfort of their sofa, thus significantly shifting buying trends in retail, e-commerce, fashion, and cosmetic industries.

According to data from April 2023, the virtual try-on market is experiencing an upward trend, with projections showing a CAGR of 15% to 20% in the coming years. This growth is fueled by ongoing research and advancements in AI, augmented reality (AR), and computer vision, along with increased smartphone penetration and a shift to online shopping following the pandemic.

Thanks to these technological advancements, one can now visualize how they would look using a particular make-up or skincare brand without applying it!

Tracing the history of innovation for the virtual try-on revolution

As the beauty industry grapples with the challenge of offering personalized experiences amid a vast array of choices, the advent of virtual try-on (VTO) technology has emerged as a beacon of hope.

In the past 10 years, research in make-up transfer techniques has significantly evolved, marking a pivotal shift from the initial foundation with 3D modeling to state-of-the-art sophisticated AI models.

Initially, 3D modeling techniques were used to perform make-up transfer. They were given a reference image of a celebrity with make-up shades. Although it worked pretty well, certain aspects still remained a challenge like maintaining high-quality facial structure, skin layer composition, and matching the ground truth color of lipstick shade/foundations.

These are some of the persisting hurdles that can’t be solved using classical image processing methods.

With the advancements in machine learning, deep learning algorithms, and augmented reality, there has been significant growth in building a viable virtual try-on model. Generative Adversarial Networks (GANs) have been at the forefront of it, due to their ability to produce high-resolution realistic images.

Over the past decade, makeup transfer research has seen rapid advancements, with some methods offering significant insights, such as BeautyGAN, which enhances image quality through pixel-level histogram losses and introduces cycle consistency and perceptual loss to maintain identity information.

For broader style transfer, such as in painting, CycleGAN was developed to train domain mapping functions with cycle consistency loss, using dual generators and discriminators, though it tends toward general rather than specific makeup styles.

Despite the innovations, GAN-based methods lack an encoder, limiting the ability to adjust makeup intensity through latent space interpolation, a crucial feature for generating multiple makeup looks from a single reference.

BeautyGlow and PSGAN have pushed the boundaries further, enabling makeup transfer even in images with large poses or expressions, overcoming the previous limitations of requiring well-aligned front perspective faces for successful makeup application.

Deep inverse graphics and learned differentiable renderers

This novel approach uses inverse graphics that deconstruct a given 3D image into its input instructions, that is then passed to an augmented reality renderer to mimic that image’s appearance in this context make-up transfer.

Traditionally, most AR renderers used for these problems are non-differentiable, meaning it’s not built to learn from its results and improve over time.

Here, the inverse graphic methods use differentiable rendering to provide feedback during training.

The framework of this model is such that it uses a graphics encoder that analyzes an image and determines the exact set of instructions (parameters) needed to achieve a desired makeup. Here, the primary function of the encoder is to translate the input image into a computable form that the AR renderer can interpret.

Further, in this model, a trainable imitator generative network is introduced to act as a graphics-based differentiable renderer. The imitator will learn to imitate the non-differentiable renderer’s output by observing how different parameters affect the final image.

To train the imitator, a sensitivity loss is proposed to closely learn the entanglement between different graphical parameters and the resulting make-up effects.

Once the imitator reliably replicates the AR renderer’s technique of understanding the nuances of parameter changes, it’s used to train the graphics encoder. The encoder then learns to generate the right set of instructions for any given image by receiving feedback on its performance.

In the inference stage, the imitator is removed from the equation and the encoder is directly used to analyze images and generate parameters. These parameters are then used by the AR renderers for the actual rendering process, making way for real-time VTO experiences.

Thus the maturing stride in research for real-time VTO capabilities has led to the adoption of these innovative techniques by the cosmetic industry, shaping consumer trends in the digital age.

How is the beauty industry redefining glamour by hopping on this trend?

Concluding our deep dive into the technological advancements, let’s shift our focus to the impact of these innovations in building new market dynamics in the beauty industry.

Thriving in the limelight, one can easily spot Perfect Corp, a Taiwanese software company rendering beauty and fashion tech solutions to the cosmetic beauty brand giants.

They’ve established dominance through cutting-edge innovations, a recent one being ‘Multi-Tone AR 3D Blush Try-On’ which gives users an opportunity to explore a wider spectrum of shades, textures, and a variety of color combinations with the introduction of 3-tone blush color virtual try-ons.

Major corporations are looking for ways to merge the convenience of digital shopping with the personalized experience of trying on makeup. Leading the charge, Walmart has collaborated with Perfect to unveil a groundbreaking virtual ‘Try-On’ experience within its iOS app.

This technological marvel allows shoppers to virtually experiment with makeup products like lip color, eye shadow, blush, and bronzer in mere seconds.

Perfect’s Facial AI solution features hyper-realistic AR-powered makeup filters, providing customers with an immersive and highly personalized shopping experience. Users have the luxury of testing over 1,400 products from an array of Walmart beauty brands, such as Almay, Black Radiance, CoverGirl, Maybelline, Revlon, and Rimmel.

In a parallel development, Microsoft has integrated Maybelline’s AI-powered ‘makeup’ filters into its Teams platform, further lowering the intersection between beauty and technology.

The Maybelline Beauty app within Teams offers users twelve unique looks at launch, complete with various blurring effects and digital makeup color options. This initiative not only enhances video conferencing aesthetics but also provides users with a detailed breakdown of the real-world Maybelline products and shades each filter replicates.

This feature empowers users to transition their virtual makeup looks into reality, seamlessly blending digital exploration with physical product usage.

Moreover, the integration of Google AR Filter technology into the beauty sector marks another significant advancement. Through a collaboration with Revieve, a Google Cloud partner, the ‘Match My Look’ and ‘Shop the Look’ features have been introduced, specifically catering to the makeup category.

Powered by Revieve’s BeautyML and the cutting-edge capabilities of Google Cloud’s Vertex AI platform, these features are designed to revolutionize how consumers discover and purchase makeup products. By providing personalized recommendations and enabling users to virtually try on products, these technologies are setting new standards in retail innovation.

These developments signal a transformative era in the cosmetic industry, where digital technology not only enhances the shopping experience but also fosters a deeper connection between brands and consumers.

Through virtual try-on capabilities, AI-powered filters, and personalized recommendations, companies like Perfect Corp, Maybelline, and Google Vertex AI are redefining the boundaries of beauty retail, offering consumers an unprecedented level of convenience, personalization, and engagement.

Effects of virtual try-on

The introduction of VTO technology has significantly impacted the cosmetic industry, transforming the way customers interact with beauty products and brands. It has led to deeper customer engagement by creating interactive experiences that foster a sense of loyalty toward a brand. A higher level of interactivity provided by VTO solutions enhances the shopping experience, making it more personalized.

It’s also a powerful tool for boosting sales as buyers are more likely to make a purchase when they can visualize how a product will look on them, reducing the uncertainty that often accompanies online shopping for cosmetics.

The agility in trend responsiveness is another significant advantage. With virtual try-ons, brands can quickly adapt to the latest beauty trends with minimal investment, keeping their offerings fresh and relevant. This capability ensures that they stay competitive in the fast-paced beauty industry, where trends can change with the seasons.

From a marketing perspective, virtual try-on technology offers a cost-effective strategy, eliminating the need for expensive photo shoots and physical samples to create predefined looks. Instead, brands can leverage digital technology to showcase their products in a variety of styles and on different skin tones, making their marketing efforts more inclusive and efficient.

In summary, virtual try-on technology in the cosmetic domain has revolutionized the shopping experience, offering benefits that range from deeper customer engagement to more effective marketing strategies. It represents a significant shift towards a more interactive, personalized, and efficient approach to beauty retail.

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govindhtech · 5 months

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Arvato Systems Google Cloud Affordable Efficient 3D Imaging

Arvato Systems using Google Cloud

Which Arvato Systems using Google Cloud in order to render 3D image manufacturing industries easier, more efficient, and less expensive

Because internet commercial strategies and internet commerce gain traction, there is an increasing need for high-quality, photo realistic 3D images. These days, judgments on what to buy may be greatly influenced by the way firms present and design their goods.

Approximately 87% of customers believe that product photographs are crucial when making an online buying choice, and another 67% believe that the caliber of the product images has a significant role in their decision to buy. Actually, a recent study found that consumers place a higher value on a product’s appearance than they do on detailed product descriptions (54%), product-specific information (63%) or both.

Imagine seeing your new sofa in your living room from a realistic three-dimensional viewpoint, with all the rich elements you want, including texture, lighting, and accessories. These 3D assets have potential use in augmented reality (AR) and virtual reality (VR) use cases and situations in addition to picture creation.

Arvato Systems, a Google Cloud Premier partner, created imagejet to facilitate the creation of 3D images. The new, cutting-edge cloud-based solution gives businesses a simple way to do high-quality mass picture production with photorealistic studio quality. It does this by using G2 virtual machines, which are powered by NVIDIA L4 Tensor Core GPUs.

Pushing the envelope and going beyond conventional norms

The post-COVID era’s surge in internet purchasing has created an unparalleled need for premium product presentation experiences that make use of studio-caliber photography. In order to stay up with the times, manufacturers, merchants, consumer packaged goods firms, and other small and medium-sized enterprises are expanding their go-to-market strategies into online channels and need a professional approach to display their products.

The limitations of traditional product photography are rapidly approaching, it is expensive, rigid, less dynamic, and not scaleable. To capture a few moments, capturing high-resolution photos of objects requires a studio setup with costly lighting and equipment. For capturing a wide variety of product textures, colors, and materials, they provide limited alternatives. When product variations proliferate and market or trend dynamics need quick product changes, this strategy cannot keep up.

However, creating a robust 3D rendering platform from the ground up involves large expenditures for infrastructure and development work, such as purchasing powerful NVIDIA graphics processing units (GPUs) and pricey third-party software licensing for 3D rendering programs.

Establishing a pipeline for picture products requires a great deal of work. It’s an endeavor that many firms can’t afford, particularly considering the hefty expenses and intricate technological requirements. Moreover, the generation of images via in-house constructions or conventional methods based on product photography is much slower, which results in a longer time to market and increases the challenge for teams to respond promptly to market developments.

How Google Cloud’s imagejet enhances the process of creating 3D images

Imagejet was created by Arvato Systems to greatly speed up the creation of 3D images. The objective was to reduce expenses and complexity while also lowering the threshold for consumers to access 3D production pipelines.

Imagejet offers the following essential functions:

simplified photo creation with 3D materials without needing to modify current photo procedures

A platform for team cooperation (customer, 3D agency)

Workflow management and approval integration

Utilizing industry-standard Product Information Management Data (PIM) and Pixar’s Universal Scene Description (USD), this production method is fully scalable and produces images at a high throughput on Google Cloud.

One asset type from a single source published over a variety of distribution channels

Consider a scenario where a client asks for 4 million images for his product catalog. In only two weeks, you could supply the needed materials using imagejet with dynamic workload allocation on Google Cloud NVIDIA L4 GPUs and optimized spot instances. It would take almost two years to finish the same request using an internal or on-site 3D manufacturing process.

Collaborating with Google Cloud to drive more creativity

Arvato Systems recognized it required a cloud partner capable of providing a wide variety of cutting-edge technologies and solid technical relationships with major competitors in the industry when it began developing imagejet. Customers of Google Cloud are guaranteed access to the newest GPUs and enough capacity thanks to Google Cloud’s strong cooperation with NVIDIA. Other essential services like Google Kubernetes Engine, Pub/Sub, Cloud Storage, Cloud memorystore for Redis, and BigQuery were also offered by Google Cloud in order to boost speed.

Arvato Systems was able to determine that NVIDIA’s L4 Tensor Core GPUs give the highest performance for its 3D rendering workloads, which involve intensive 32-bit (floating point) operations, thanks to close engagement with the Google Cloud Customer Engineering team.

In comparison to the NVIDIA A100 Tensor Core GPUs that were evaluated before, Arvato Systems was able to increase imagejet’s 3D rendering speed by 160% and decrease rendering expenses by 75% by using the most recent NVIDIA L4 GPUs.

Through Google collaboration with Arvato Systems, we have not only advanced technology but also taken a step in the direction of reducing energy use. They have greatly increased the energy efficiency of your 3D rendering operations by using NVIDIA L4 GPUs on Google Cloud, said Anna-Maria Martini, head of client engineering for Media and Entertainment at Google Cloud. “This demonstrates how cutting-edge technology can not only improve performance but also significantly contribute to lowering the ecological footprint an increasingly significant factor in the digital age.”

Arvato Systems has a number of advantages, including a superior cost-performance ratio, reliable availability of needed capacities inside the EU, and access to a broader range of Google Cloud services and products. By providing its clients with a flexible utilization-based pricing model, imagejet leverages the great scalability of the underlying Google Cloud infrastructure to support numerous 3D mass production projects, minimize technological complexity, and save human costs.

Facilitating the development of high-quality 3D images more quickly and easily

The creation of imagejet represents a significant turning point in the way companies can improve and change their product presentations, which are increasingly an essential component of their commercial strategies. Imagejet provides a new method of working that allows the creation of high-quality 3D pictures quicker, more inventive, and more affordable. This is due to the ongoing progress of cloud technologies as well as the growing desire for immersive and more interactive shopping experiences.

Christian Scholz, vice president of cloud and business transformation at Arvato Systems, said, “Our solution imagejet will revolutionize the way artists, agencies, marketing departments, gamers, developers, and more work thanks to Google Cloud.” “We provide our clients with powerful tools so they can pool their resources and build virtual worlds.”