This work addresses two problems that stall AI training programmes: not enough data, and almost none of it covering the long tail. Usingprocedural content generation, we built a scene engine that produces physically plausible, semantically rich environments without a linear increase in artist time.
The economics of a PCG + DCC pipeline
The shift is not a tooling upgrade. It changes what the team actually produces.
A traditional pipeline producesone model per unit of effort. Cost per asset is roughly fixed, so total cost scales linearly with quantity: the larger the programme, the larger the bill.
A PCG + DCC pipeline producesa repeatable production process. The upfront investment is higher, but once the rules, constraints and asset kits exist, additional scenes cost close to nothing. Beyond a threshold that most serious data programmes cross quickly, the total cost curve flattens while output keeps growing.
| Traditional pipeline | PCG + DCC pipeline | |
|---|---|---|
| Unit of work | A single finished model | A production rule set |
| Cost curve | Linear in scene count | Front-loaded, then near-flat |
| Variation | Manual, and expensive to expand | Parameterised, generated on demand |
| Reproducibility | Depends on file versioning | Deterministic from a seed |
| Long-tail coverage | Whatever the schedule allowed | Sampled deliberately from the distribution |
Capabilities
| Area | What you get |
|---|---|
| Scene generation | Road networks, buildings, terrain, vegetation and interiors composed from rule sets and asset kits |
| Semantic control | Generation driven by the distribution you specify, so rare conditions can be over-sampled on purpose |
| Physical validity | Collision, support and navigability checks before a scene enters the dataset |
| Engine targets | Unreal Engine 5 and Unity, sharing one scene description |
| Sim-to-real | Domain randomisation across materials, lighting and weather to narrow the transfer gap |
Where it fits
Programmes that have exhausted what field capture can economically provide, and now need thousands of controlled variations of the scenarios that actually break their model. Because the generator is driven by an explicit distribution, coverage becomes a number you can report rather than an assumption.
Deliverables
- Rule sets and asset kits for your scenario family
- The generation service, targeting your engine of choice
- Validity and coverage reports per generated batch
- Integration with the synthetic data capture pipeline
Talk to us
Tell us which scenarios your current dataset under-represents, and we will scope the generation rules needed to close the gap.