[Style3D Research] Wait, Have AI Models Really Come This Far?

Main author: @Gaofeng He - Senior Algorithm Engineer at Style3D

Digital fashion is no longer a new concept. Digital clothing and virtual models have become integral to the fashion industry, with more brands and designers adopting digital technologies for design, manufacturing, and promotion. However, with the rise of generative AI, the digital fashion landscape is experiencing yet another wave of technological transformation, and AI models are at the forefront of this change. By leveraging deep learning, computer vision, and models like Generative Adversarial Networks (GANs) and Diffusion Models, it has become possible to generate highly realistic virtual models and customize them for diverse market demands—from skin tones and body types to hairstyles.

For example, NVIDIA's “ThisPersonDoesNotExist” website showcased AI's ability to generate human faces using extensive facial datasets. While early results appeared unnatural, increased training refined these faces to be nearly indistinguishable from real people.

Japan’s DataGrid took this a step further by generating not just faces but entire virtual human figures using AI technology.

However, challenges remain. Creating highly detailed and natural-looking virtual models with smooth and realistic movements is still a complex task. Enhancing their realism efficiently has become a critical industry goal. To address this, Style3D has invested heavily in R&D, diving deep into the evolution of generative AI technologies and achieving innovative breakthroughs.

Part I: The Evolution of Generative AI in Digital Models

GAN: The Leap in Generative AI

In 2014, Ian Goodfellow introduced Generative Adversarial Networks (GANs), a model concept based on adversarial training between a generator and a discriminator. Progressive GAN later improved this by using progressive training to create high-resolution images up to 1024x1024 pixels, capturing fine facial details like eyes, noses, and skin textures with lifelike precision.

Despite its advancements, GANs still face challenges such as image distortion at high resolutions and limitations in finely controlling specific facial features.

Diffusion Models: A Breakthrough

Inspired by physical diffusion processes, Diffusion Models surpassed traditional GAN frameworks by enabling AI to reconstruct images from noisy, disordered data. These models combine mathematical diffusion processes with deep learning, significantly enhancing image quality and structural consistency.

Diffusion models train AI to understand and reverse information transformations, enabling powerful denoising capabilities to generate realistic images and videos from noise. By combining the principles of physical diffusion processes with deep learning, these models have overcome critical technical barriers in image generation and information reconstruction.

Stable Diffusion

Stable Diffusion employs a generation process based on diffusion models. Its core principle lies in a reverse process that gradually transforms pure noise into a clear image. This approach is characterized by enhanced control and stability. The generation process typically consists of two main stages:

As shown in the diagram, Stable Diffusion is an image generation algorithm built upon diffusion models, incorporating several optimizations. It is primarily designed to convert textual descriptions into high-quality images. The algorithm can be broken down into the following key steps:

Image Encoding → Noise Addition and Diffusion Process → Conditional Generation → Denoising Process → Image Decoding

However, challenges like high computational demands and difficulties in generating precise model details (e.g., fingers and toes) persist.

DiT

DiT (Diffusion Transformer) combines diffusion models with Transformer architecture addressing previous limitations in image generation quality, speed, and diversity. It has emerged as a leading solution for complex generative tasks.

Part II: Style3D’s AI Innovation in Digital Models

Style3D AI leverages generative AI technology, combined with advanced 3D modeling and graphic rendering techniques, to achieve an efficient production workflow from digital models to commercial photoshoot models. This technology not only generates highly realistic virtual model images but also, through integration with 3D modeling, accurately recreates human body shapes, material textures, and dynamic expressions.

By leveraging a unique training framework and introducing the Texture-Preserving Attention Control (TAC) mechanism, Style3D ensures consistent structural and texture information during the image generation process. This addresses the longstanding challenge of bridging rendered visuals with realistic fashion imagery.

In 2024, Style3D’s research was recognized at NeurIPS, one of the world’s leading AI conferences. In same year, we also earned a patent titled "A Personalized Rendering Method and System."

Part III: Practical Applications: Replacing Traditional Photoshoots

Style3D AI optimizes lighting effects, garment details, and scene realism to produce studio-level visuals for both static displays and dynamic videos. Whether showcasing multiple models or diverse scenarios, it delivers professional-grade results while significantly reducing time and costs.

Through the AI-enhanced renderer (iWish), Style3D enables the rapid generation of virtual models tailored to specific faces, ages, genders, skin tones, and poses. From facial expressions to clothing fits, every detail is refined to ensure consistency and realism across frames.

Notably, AI-enhanced renderer (iWish) can produce consistent consecutive frames with highly uniform features, including hairstyle, facial shape, expressions, and body proportions, simply by adjusting the virtual camera angle. This allows brands and designers to showcase clothing on virtual models with exceptional consistency and stability from multiple perspectives.

With Style3D MixMatch, our virtual styling software, we enhance real-time 3D fashion styling, integrating AI to deliver highly realistic outfit combinations.

Style3D's AI technology breakthrough offers innovative solutions for fashion e-commerce, advertising creativity, fashion design workflows, and virtual showcases. It enables rapid responses to market demands while meeting diverse and personalized customization needs, opening up new possibilities for the integration of digital models and commercial photography.

Part IV: The Future of AI Models in Digital Fashion

AI models are set to become indispensable in the digital fashion industry, offering brands more efficient, innovative solutions while driving the industry toward personalization, globalization, and sustainability. Whether in e-commerce, fashion advertising, or design workflows, the potential for AI models is vast. As technology and fashion continue to converge, we are on the brink of a transformative era in fashion innovation.

Stay tuned as Style3D continues to push the boundaries of AI-driven fashion technology, unlocking endless possibilities for brands, designers, and fashion professionals worldwide.