AI product visualization is the new era of product photography.

Traditional photoshoots have always filled the need for product visualizations in e-commerce, from the latest vehicle upgrades to different colors of running shoes. Yet, traditional photoshoots can be costly and difficult to schedule. Today, AI advancements in product visualization are pushing boundaries by doing it all faster and more efficiently with fewer overhead resources. With the right tools and expertise, AI product visualization can drive rapid image creation and display product differentiation, all for a fraction of the cost. 

AI product visualization: An alternative to traditional photoshoots  

Many businesses face challenges when it comes to visually showcasing product variations, especially when dealing with multiple combinations of models, colors, and features. Organizing traditional photoshoots can be costly, time-consuming, and often impractical, with expenses running high and limited returns regarding usable images. 

Generative AI offers a powerful solution to this problem. By leveraging high-fidelity 3D renderings, businesses can create dynamic, customizable visuals that allow customers to see various configurations of products. This enhances the buying experience by providing a clear, detailed view of the product, all while avoiding the logistical complexities of traditional photography.

The need for high-quality product photography isn’t going away. Yet, tight timelines and resource-heavy requests have historically been a roadblock for brands looking to produce results quickly. Photo shoots also require a heavy lift in terms of models, photographers, lighting equipment, and more. 

Using AI at a high level significantly reduces timelines and resource demands compared to traditional photo shoots. It enables the creation of high-quality assets without the need for on-site photographers or time-intensive production processes.

Speed to delivery

While cost is definitely the main driver in most cases, speed to delivery is a major factor. Looking at the cost basis of an individual asset and considering the iterative process, the savings become clear. When assessing the cost per deliverable, the value becomes more clear. Once the system is in place, brands receive assets faster and with greater accuracy.

How 3D product visualization works

AI serves as the foundation for starting a solution, but the final outcome relies on experts who apply precision, scrutiny, and expertise to perfect the process. For image generation projects like these, creative consultants frequently utilize Stable Diffusion as a platform. Although open versions of Stable Diffusion exist, achieving consistent, high-quality results demands a more sophisticated approach. By fine-tuning specific parameters, the workflow ensures the generated images meet the desired standards of realism and accuracy, delivering exceptional visual quality.

To train an AI model to accurately render accessory images, clients need a large dataset of images covering various angles, elevations, and configurations. While 3D models play a role in this process, they are not directly used to train the model. Instead, hundreds of two-dimensional images are exported from the 3D models, capturing all possible perspectives. These images, paired with text descriptions, form the dataset.

Each image has an associated file that specifies its angle, elevation, and visible features. This information then educates the AI model to recognize and replicate the accessory accurately.

From there, prompt engineers describe the desired image in detail, specifying both what the image should include and exclude. This process of fine-tuning the language and dataset is as much an art as it is a science. This hands-on approach allows the team to create highly accurate representations of vehicles and accessories seamlessly integrated into realistic settings.

Industry-specific applications for AI product visualization

The opportunities for AI product visualization abound in various industries, with automotive and e-commerce offering two examples. 

Opportunities for AI in automotive

One leading automotive brand with billions in annual revenue from accessories continues to grapple with effectively showcasing its expansive product range. Relying on static imagery, their website struggles to provide customers with a dynamic way to explore accessories—like floorboards, mats, dog crates, and window shades—spanning countless models, years, versions, and color combinations. This limitation not only hinders their sales growth but also intensifies their search for cutting-edge solutions to keep pace with tech-savvy consumers who expect effortless customization and immersive visualization experiences. The opportunity to bridge this gap could transform both their sales potential and customer engagement.

Equally important, more than 80 percent of accessory purchases are made in the first 120 days of vehicle ownership (and the search for accessories actually begins before the consumer completes the vehicle transaction in the dealership). That said, it was integral for this brand to keep its customers in the buying cycle and not lose them because of a lack of visual assets.

Our solution directly addresses this by combining 3D renderings of both vehicles and accessories. Through the merging of these renderings, enhanced with proprietary technology, consumers can seamlessly select and visualize accessories of their choice for limitless virtual vehicle customization. This innovative approach expands the brand's ability to showcase its offerings, elevates the customer experience, and positions the brand to achieve a measurable return on investment within the first year.

Opportunities for AI in e-commerce

E-commerce opportunities are another example of where this technology could be used. Especially when it comes to celebrity modeling. Consider an example of a celebrity athlete contracted to model a new line of athletic apparel. With new AI product visualization tech, creative could transcend meetings, mood boards, and creative pitches and instead move quickly into AI-generated images of exactly what the brand wants to propose. Creative leaders could quickly present an image of the persona with their likeness, decked out in the company’s branding, primed for approval—and move forward to the actual photo shoot, already prepped with the shot list and ultimate vision in mind. They’ve effectively cut out guesswork to get to the heart of the deliverable.  

Taking this a step further is the opportunity to put more products out into the marketplace faster.  In this case, AI applies apparel or products to the actual models or mannequins using precise data for accurate proportions and true-color depictions. This is a big advance beyond simply prompting generative AI to guess at an image.

Hyper-customization and impacts on customer retention

This type of visualization also harbors immense power for image customization. 

Take an example of a customer buying an orange sportscar. Every time the customer logs into their account, every image that they see is a bright orange sportscar, with every accessory, color, and trim perfectly matched, from upgraded wheels to spoilers to roof racks. AI visualization can accommodate this type of hyper-personalization.

Brands can also use customer data to suggest additional accessories depending on the season or geographic location, such as presenting options for the vehicle with a ski rack for customers in mountainous regions. There could also be digital accessories for vehicles, like additional coverage, subscriptions, and additional data performance upgrades to the actual traction of the vehicle that could become useful in certain scenarios.

This type of extreme AI personalization for a purchased item allows brands to deliver meaningful and relevant products or accessories tailored to the customer’s needs—virtually in real time.

AI customization as an internal tool for innovation

The best creative innovations are focused on building bespoke experiences tailored to each client’s unique needs, which can vary significantly. One key consideration is determining the role and purpose of an image—what it will be used for and how it fits into the broader context. 

For instance, some clients may not be working on consumer-facing or marketing initiatives, but they still need effective visual tools to make decisions. One example is executive presentations. Clients are often challenged to create mood boards, take photos, or use tools like Photoshop to piece together visuals, which can be time consuming. 

As one of our clients once pointed out, success often depends on an executive’s ability to visualize the concept, which isn’t always effective. If they can explore building AI models that streamline the process, it will save time and make their presentations clearer and more impactful.

AI product visualization: a powerful solution 

AI product visualization offers a powerful solution to the long-standing challenges of traditional product photography, moving beyond the bottlenecks of costly and time-consuming photoshoots to deliver authentic and accurate product images more quickly. This technology empowers brands and retailers to rapidly generate a high volume of customized visuals, realizing significant cost savings in the long run. More than simply replacing traditional methods, AI visualization makes the process more iterative and accessible, serving a broader range of needs. 

The future of product visualization lies in the continued evolution of AI-powered tools, but achieving their true impact requires more than just the right technology. It calls for expertise, a well-defined strategic vision, and an iterative approach to refinement. Partnering with Aquent Studios ensures you have the expertise and support needed to harness these innovations and create meaningful, lasting results.