SynplerHuman: Simplified Synthetic Data Generation for Estimating 3D Human Pose and Shape

Synthetic training data is widely used for 3D human pose and shape estimation and recent datasets invest large efforts in increasingly realistic humans, clothing, and environments. However, these improvements are usually introduced together, making it difficult to isolate which factors matter most for real-world generalization. To remedy this problem, we present SynplerHuman, a controllable, open-source procedural generator of data for 3D human pose and shape estimation". Our generator procedurally generates bodies, clothing, materials, and scene variations, enabling unlimited data generation without relying on external curated 3D appearance asset libraries. Using SynplerHuman, we study what factors of realism matter in synthetic data generation. We find that articulation choices have the largest impact on performance, while appearance variations produce relatively small changes. Finally, we demonstrate that SynplerHuman produces data that is effective when combined with existing datasets, and is competitive with synthetic datasets such as SynBody.