Digital twin rendering and AI-driven compositing are redefining product content pipelines and transforming operational workflows in global brand environments. These technologies offer precision, speed, and control at scale, supporting everything from e-commerce to experiential marketing.
In the high-stakes environment of global branding, consistency is more than a visual standard. It is a non-negotiable business imperative. For Coca-Cola, which delivers over 2.2 billion servings daily across more than 200 markets, that imperative has long presented a challenge: producing marketing and e-commerce imagery at scale without compromising precision.
The answer lies in the convergence of digital twins, OpenUSD, and NVIDIA Omniverse. By embedding a new layer of AI-enabled automation into its content production workflows, Coca-Cola has transitioned from fragmented, reactive image creation to a centralised control and repeatable quality model. The scale and sophistication of this shift have been enabled through a long-standing collaboration with Grip/INDG, whose platform builds on twenty-five years of compositing, rendering, and automation expertise.
Data, not photos
The digital transformation of Coca-Cola’s image assets did not begin with AI. It began with a white paper. Rudy Martinez, now Global Director of Packaging Graphics and Product Visualisation Presentation at Coca-Cola, first encountered Grip’s vision over a decade ago. “They were proposing to use software to replace traditional product photography with 3D-generated imagery, higher quality, faster, and significantly more cost-effective,” he recalls. “At the time, the idea remained just that. Today, it is the foundation of Coca-Cola’s digital asset strategy, with 95 per cent of e-commerce images generated using 3D digital twins.”
Building that system, however, required far more than rendering technology. “Digitising a portfolio the size of Coca-Cola’s, with 138 years of legacy, disconnected systems, and fragmented data, is a monumental task,” Martinez explains. “It took years to consolidate specifications, codify visual standards, and reach a level of completeness where every variant, label, and SKU could be rendered at will.
“Everyone knows Coke red, but not many know that even the specific green used in our bottles is codified. Even the foam at the top of a bottle has to be precisely rendered.” That level of control is now embedded across the company’s global content workflows, eliminating historical inconsistencies and inefficiencies that plagued decentralised production.
One asset, many uses
The creation of high-fidelity digital twins enables much more than ecommerce photography. Coca-Cola’s FIFA campaign demonstrated the power of shoot-once, scale-anywhere content. By compositing 3D assets into localised scenes using AI-assisted control tools, the company can now adapt marketing visuals for hundreds of markets without multiple production cycles.
That model is now expanding to support campaigns and planogram simulations, inventory management, and in-store visualisation. Each use case is built on the same foundation: a digital asset system governed by strict visual rules, rendered on demand through NVIDIA Omniverse, and enhanced by generative AI. “Imagine when we used to do this conventionally and take product photography,” Martinez adds. “Managing consistency in 200 markets across 200 brands was extremely hard. Because of that model, we now have governance for these specifications at the global level. They are signed off and sanctioned, and then we just execute.”
Infrastructure for future content
At the core of Grip’s approach is a modular content engine. Daniël Haveman, CTO of Grip/INDG, describes it as a system that decomposes scenes into configurable components, products, backgrounds, AI-generated elements, and even lighting parameters, each governed by its own logic. “Our core technologies include digital twin rendering for high-fidelity visuals, a compositing engine that blends rendered assets with photography and footage, and AI controllers that customise image components while keeping everything on brand,” Haveman explains.
The system is designed to operate at an enterprise scale. Clients can access tools via a graphical interface or integrate them directly into their infrastructure through an API. In Coca-Cola’s case, that means dynamically rendering asset variants across hundreds of campaigns, SKUs, and regions without breaking brand compliance. “Creating an image is far more complex than placing a product on a background,” Haveman continues. “We start with a clean base plate, add the digital twin at the right angle and lighting, apply the label artwork, calculate reflections and refractions, and then blend it together in a composite.”
NVIDIA’s OpenUSD framework is central in ensuring that complexity remains manageable. The ability to manage multiple versions, enable real-time collaboration, and support diverse output formats, from high-end renderers to real-time configurators, makes the system scalable and adaptable.
Embedding intelligence in the image
One of the most significant innovations is the use of generative AI within the production pipeline. It is not about replacing human creativity but accelerating manual tasks and maintaining consistency at speed. “Generative AI is already impacting the pipeline, particularly in clean-up tasks like creating background plates,” Haveman says. “Where artists once manually rebuilt areas in Photoshop, they now use tools like Adobe Firefly.”
More critically, the intelligence baked into the system allows for continual refinement. Visual specifications, droplet size, Coke liquid hue, and lighting balance are codified into the engine. The result is visual output that appears photographic but is entirely digital and consistent. “That is the strength of the platform we have built,” Haveman concludes. “A bottle that looks like it was photographed is actually a digital twin rendered and composited with precise control.”
Beyond visualisation
As Coca-Cola continues evolving its strategy, the emphasis shifts from completeness to performance. Image assets are now analysed for consumer engagement, allowing the team to optimise content quality and business outcomes.
Martinez sees this as a moment of inflexion. “With AI and digital twins converging, we see emerging use cases in-store planning, inventory management, and customer engagement,” he says. “We do not have all the answers yet, but we are here to meet potential partners and explore what is next.”
The ambition is not to create images, but to build infrastructure; infrastructure that can support real-time, brand-consistent content at global scale, while enabling entirely new applications in logistics, supply chain, and customer experience. As Martinez puts it, “Future-proofing our digital twin strategy means thinking about governance, security, and flexibility. We need partners who understand this space and can help us evolve.”
The story of Coca-Cola and Grip/INDG is not one of aesthetic improvement. It is a story of operational reinvention, where AI, compositing engines, and OpenUSD frameworks converge to turn content production into a strategic asset. The end product may look like a Coke bottle, but underneath it is an intelligent system optimised for scale, precision, and the future of brand experience.
