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Shutterstock and Getty Images announced different kinds of generative-AI products in July 2024. Shutterstock introduced an enterprise Generative 3D API for creating editable 3D assets and 360-degree HDRi environments. Getty Images upgraded its 2D image-generation service with faster output, camera controls, composition guidance, 4K upscaling and enterprise fine-tuning.
Both products used NVIDIA’s Edify visual-AI architecture, but NVIDIA supplied the underlying technology rather than operating the customer-facing Shutterstock or Getty services. Because NVIDIA later said Edify was no longer available as an NVIDIA NIM microservice preview from June 6, 2025, the launch features below should be understood as historical unless a current vendor page confirms otherwise.
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- 3D TABLET
Two products, two very different workflows
The July 29–30, 2024 announcements are easy to misread as a single generative-AI launch. They were not. Shutterstock targeted people who need 3D geometry; Getty Images targeted people who need finished or controllable 2D imagery.
| Service | Primary output | Best suited to |
|---|---|---|
| Shutterstock Generative 3D | Editable 3D assets and 360 HDRi environments | 3D artists, game and XR developers, virtual production, product visualization and digital twins |
| Generative AI by Getty Images | 2D generated images | Advertisers, agencies, marketers and brands needing controlled visual variations |
NVIDIA described both services as examples of how its visual-AI technology could support commercial creative workflows, from model customization to accelerated inference.
What Shutterstock’s Generative 3D API did
At launch, Shutterstock described its Generative 3D API as a commercial beta for enterprise customers. It supported both text-to-3D and image-to-3D generation, allowing a user to describe an object or provide a reference image.
The announced workflow was deliberately preview-first:
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- A text or image prompt generated a single 3D preview.
- The user reviewed or adjusted the preview.
- An approved preview was converted into a higher-quality 3D asset.
- The resulting asset could be edited in a digital-content-creation application.
NVIDIA said an initial preview could arrive in as little as approximately 10 seconds. That was a launch claim and best-case estimate, not a guaranteed production service-level agreement.
The service was described as producing editable geometry in multiple common 3D formats, with physically based rendering materials such as wood, concrete and leather. Shutterstock also demonstrated workflows involving Blender and other creative tools. The intended benefit was not to eliminate 3D production, but to accelerate the creation of props, scene elements and early concepts.
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Potential applications included virtual production, extended reality, gaming, product visualization, digital twins, rapid prototyping and 3D printing. A generated model could provide a useful starting point, but the launch announcements did not establish that every asset would have production-ready topology, clean UVs, accurate dimensions, reliable scale or animation-ready rigging.
The separate 360 HDRi generator
Shutterstock also announced a 360 HDRi generation capability. An HDRi panorama is primarily used as a lighting and background resource: it can illuminate a virtual object and provide reflections and environmental context.
NVIDIA reported output of up to 16K resolution. That can be valuable for product renders, vehicles, characters and virtual sets because a team may not need to photograph a physical 360-degree location.
However, an HDRi panorama is not the same as a complete 3D world. It does not automatically supply navigable geometry, collision surfaces, physically accurate scene depth or a complete digital twin. Teams using one for moving cameras or interactive applications would still need to evaluate parallax, shadows, horizon seams, light direction and reflection consistency.
What Getty Images upgraded
Getty’s product remained a 2D image-generation service. According to NVIDIA’s announcement, the newer Edify-based model generated images approximately twice as fast as its predecessor and could produce four images in about six seconds.
The upgrade also added more control over the visual result:
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- Camera controls, including focal length and depth of field.
- Sketch guidance for influencing composition.
- Depth-map conditioning for providing structural information.
- Upscaling to 4K.
- Enterprise fine-tuning using a company’s own data or visual style.
Focal-length and depth-of-field controls address a common weakness of prompt-only generation: it is difficult to request a specific photographic perspective and get predictable results. These controls may help an agency create campaign imagery with more consistent framing and lens character. They do not, however, prove exact real-world lens matching or pixel-level consistency between generations.
Sketches and depth maps add another layer of control. Instead of relying only on words, a creative team can provide structural guidance about placement, perspective or composition. Fine-tuning serves a different purpose: it adapts a model to an organization’s preferred style, products or proprietary visual data.
Getty said its Generative AI service was available through Getty Images, iStock and an API. The audience was therefore broader than individual experimentation: agencies and enterprise marketing teams could use it for campaign variations, branded imagery and other repeatable production work.
Why NVIDIA Edify mattered
NVIDIA presented Edify as a multimodal architecture capable of supporting images, video, 3D assets, 360 HDRi environments and physically based rendering materials. Its broader proposition was that companies could train or customize models with licensed or proprietary data, optimize them for inference and deploy them through NVIDIA’s infrastructure.
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The surrounding NVIDIA stack included tools and services such as NIM accelerated inference microservices, DGX Cloud and AI Foundry/Picasso. That makes “NVIDIA-powered” a description of the technical foundation, not a claim that NVIDIA launched or operated Shutterstock’s and Getty’s finished products.
The partnership history goes back to March 2023, when Shutterstock and NVIDIA announced plans to train custom 3D models using Shutterstock content and NVIDIA Picasso services. In March 2024, NVIDIA described Shutterstock’s 3D generation as entering early access and highlighted Getty’s planned fine-tuning and image controls.
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Shutterstock said its 3D model was trained exclusively on Shutterstock content, including more than 500,000 3D models and more than 650 million images. Those are company-reported figures. Getty and NVIDIA described Getty’s foundational model as commercially safe. Such statements are meaningful signals about intended data provenance and enterprise positioning, but they are not blanket legal guarantees.
What “licensed” and “commercially safe” do—and do not—mean
Training on licensed material is not identical to guaranteeing that every output is free of third-party rights. Buyers still need to examine the applicable license, indemnity, privacy, retention and content-use terms.
Separate questions can arise from:
- User-supplied reference images and proprietary materials.
- Logos, trademarks and recognizable products.
- Faces, likenesses and locations.
- Confidential data entered into prompts or fine-tuning workflows.
- Outputs that resemble protected designs or other customers’ work.
“Ethically sourced” describes Shutterstock’s claimed sourcing approach; it does not independently establish that the model has never been subject to a legal or ethical dispute. Enterprise teams should treat contractual protections—not marketing language alone—as the basis for a deployment decision.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where generative 3D still needs human production work
The launch material emphasized speed, editable outputs and workflow integration, but did not promise guaranteed engineering accuracy or production-ready results for every asset. Before adopting a generated model, a team should inspect:
- Topology, non-manifold geometry and polygon distribution.
- UV layout, texture resolution, normals and material assignments.
- Scale, dimensions and alignment with the reference object.
- Thin structures, holes, cables, transparent surfaces and repeated patterns.
- Consistency when several assets must appear together.
- Rigging, deformation and animation requirements.
That makes the most defensible description of the Shutterstock service an accelerated 3D starting point, not an automatic replacement for modeling, retopology, texturing, look development or technical validation.
How to evaluate Getty’s image workflow
Marketing and agency teams should test the system against their actual production requirements rather than relying on generation speed alone. Useful checks include:
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- Whether hands, faces, typography, logos and product details remain accurate.
- Whether focal-length and depth-of-field settings change images predictably.
- Whether sketch and depth-map guidance preserves important object placement.
- Whether repeated generations maintain a campaign’s visual identity.
- Whether fine-tuning improves brand consistency without overfitting or reducing creative range.
- Whether generated people, properties or locations create separate likeness or rights concerns.
Fine-tuning can strengthen brand consistency, but it does not automatically clear the rights to every training input, output or depicted subject.
Timeline
- March 21, 2023: Shutterstock and NVIDIA announced their custom 3D-model partnership.
- March 18, 2024: NVIDIA announced Shutterstock 3D early access and new Getty customization and control features.
- July 29, 2024: NVIDIA reported Shutterstock’s Generative 3D commercial-beta launch and Getty’s upgraded service.
- July 30, 2024: Shutterstock published its own launch announcement, including enterprise access and historical credit-pack pricing starting at $25.
- June 6, 2025: NVIDIA later stated that Edify was no longer available as an NVIDIA NIM microservice preview.
What this means for buyers in 2026
The most important purchasing question is no longer simply whether the 2024 feature list sounds useful. It is whether the relevant service is still available, under what product name, through which access method and with what current terms.
Before committing to a workflow, ask:
- Is access currently self-serve, API-only or enterprise-sales-led?
- Which formats are returned, and are meshes, UVs and materials suitable for the pipeline?
- Are generated assets exclusive, or could similar outputs be supplied to other customers?
- What indemnity and commercial-use rights are included?
- Are prompts, reference images or fine-tuning data retained or reused?
- What are the rate limits, usage fees, minimum commitments and support terms?
- Does the service integrate with the team’s Blender, OpenUSD, Omniverse or game-engine workflow?
NVIDIA’s Edify page now carries an editor’s note saying Edify was no longer available as an NVIDIA NIM microservice preview from June 6, 2025, and directs users toward NVIDIA’s current model catalog. That notice means the historical NVIDIA test endpoint and the original launch announcement should not be treated as proof of unchanged access in 2026. The $25 Shutterstock credit-pack figure is likewise historical July 2024 pricing, not a verified current price.
Bottom line
Shutterstock’s announcement was about generating editable 3D objects and lighting environments; Getty’s was about making 2D image generation faster and more controllable. NVIDIA’s Edify architecture connected the two initiatives, but the products served different users and were delivered by different companies.
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For artists and enterprises, the opportunity was faster prototyping, richer visual control and API-based production. The practical decision still depends on cleanup requirements, rights terms, integration, quality testing and—especially in 2026—confirmation that the relevant service and endpoint remain available.
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