Photorealistic synthetic data for computer vision,
built from production 3D environments

Depending on the project, a delivery can include instance identity, semantic masks, object IDs, visible 2D boxes, oriented 3D bounding boxes, surface normals, world position, alpha, material-related render passes, camera metadata and validated depth. The final set is defined by the target task and the tested export pipeline.

Yes. Object naming and semantic classes can be mapped to a client taxonomy, including rules for class granularity, occlusion, visibility and fallback labels. The machine-readable schema and acceptance tests are agreed before production.

Production work is centered on 3ds Max with Corona or V-Ray, with Blender and Cycles or Unreal Engine 5 available when required. Scene and data formats are selected per project. A format such as COCO, YOLO, KITTI or OpenLABEL is only promised after the required fields and exporter have been implemented and validated for that delivery.

Verification can cover physical units, coordinate conventions, transforms, semantic coverage, instance separation, material tracking, cameras, lighting and render-pass configuration. The checks, thresholds, advisories and output manifests are documented with the delivery.

AI/ML use is excluded from standard Evermotion product licenses. Approved training, fine-tuning, evaluation or dataset use requires a separate, project-specific B2B agreement defining the content, permitted uses, formats and downstream rights.

You do. The license can grant the client full rights to use everything that results from training - the model, its weights and its outputs. Evermotion keeps ownership of the source 3D content itself.

The library was built by our own studio since 2005 - modeled, textured and lit in-house for commercial rendering. Nothing is scraped and no third-party datasets are involved, which is why we can document provenance and put the license in front of your legal team.

Yes. For synthetic data platforms and resellers, scenes or extracted data can be delivered without Evermotion attribution, under NDA, as part of the project agreement - your clients see your brand, not ours.

Per asset - a model or a scene. The rate depends on three things: the scope of the license, the size of the order and the adaptation work needed to meet your specification. You get a production estimate with the feasibility response, and the terms are agreed individually.

No. Photorealistic assets and controlled scene variation can help address appearance and content gaps, but model accuracy and generalization must be evaluated on representative real-world data and your downstream benchmark.

Synthetic data works best alongside real data, not as a full replacement. Combining synthetic renders with real images gives a model trained on large volumes of high quality training data, plus enough real examples to stay grounded in the deployment domain. Rendered frames also cover rare situations that are hard to capture in real life, so real and synthetic data together beat either source alone. Accuracy and sim-to-real transfer are measured on your real-world benchmark, not assumed.

Synthetic data gives deep learning models annotated data to learn from without hand-labeling, so computer vision models train on exact labels from the first run. Rendered from virtual environments, it provides diverse datasets that cover real world scenarios and rare edge cases which are hard to collect from real footage. Because the images come from computer simulations of 3D scenes, you control the content and can match the statistical properties of your target domain.

Most teams use it for AI model training alongside real images, training models on this artificially generated data as a complement to real captures and data augmentation, not a replacement. Evermotion supplies the prepared scenes; the renders run in your own pipeline.

Yes. Labels are generated from the 3D scene, so masks, boxes, classes and depth come out pixel-perfect - perfectly labeled datasets straight from the render, with no manual labeling. Producing such data by hand is slow and adds label noise, so reading the visual data and its labels directly from the scene removes both the annotation cost and that noise.

Synthetic data generation for computer vision follows two broad approaches that create synthetic data in different ways: rendering from explicit 3D scenes, or synthesizing synthetic images from generative models. Evermotion scenes are built for 3D rendering, which reads exact, pixel-perfect ground truth straight from the scene and keeps full control over object positions, camera angles and lighting.

Evermotion renders from explicit 3D scenes, not from generative AI. Generative adversarial networks, diffusion models and neural style transfer synthesize pixels from learned distributions, which makes exact ground truth hard to guarantee. A rendered 3D scene knows every object, material and camera, so labels are exact and you keep full control over object positions, camera angles and lighting.

Yes. Synthetic scenes contain no real people or captured locations, so there are no privacy concerns carried over from real footage. Because you set the content distribution, you can balance under-represented cases and reduce bias instead of inheriting whatever real data collection happened to capture.