A Hybrid Approach for Closing the Sim2real Appearance Gap in Game Engine Synthetic Datasets

Generative models are increasingly used in video game engines to enhance the photorealism of rendered images for visual synthetic data generation and simulation applications. However, they often introduce artifacts that alter the content of the original rendered scenes and require high computational resources, which limit their utilization for the photorealism enhancement of training and evaluation data, as well as their integration in the rendering pipelines of game engines. In this paper, we propose Hybrid Patch Enhanced Realism Generative Adversarial Network (HyPER-GAN), a hybrid image-to-image translation framework that is based on a lightweight U-Net-style generator capable of performing real-time inference. The framework is trained using paired rendered and photorealism-enhanced images, complemented by a novel hybrid training strategy that incorporates matched patches from unpaired real-world images to improve content preservation and further enhance the visual realism that can be achieved by the lightweight generator. Experimental results demonstrate that HyPER-GAN achieves a 6x increase in frames per second at 1080p in comparison with state-of-the-art lightweight paired image-to-image translation methods, while also increasing, in both within- and cross-engine evaluations, the photorealism of the rendered images without significantly compromising semantic consistency. Moreover, it is illustrated that HyPER-GAN maintains temporal consistency and that the proposed hybrid training strategy improves content preservation and visual realism in within-engine and increases the robustness in cross-engine evaluations compared to training the framework solely with paired rendered and photorealism-enhanced images. Code and pretrained models are publicly available at: https://github.com/stefanos50/HyPER-GAN

Deep learning computer vision for scientific applications requires collecting and annotating large datasets in a laborious, expensive and error-prone process. Synthetic data generation through 3D modelling and rendering may simplify this process and increase the accuracy of annotations by generating them programmatically. However, minimising the domain gap between real and synthetic images visually is subjective and lacks systematic quantitative guidance. We present GraNatPy, a Python package with metrics to guide improvement of the rendered scene. We show that quantifiable increase in realism, diversity and size of rendered dataset correlates with improved visual perception of the scene and higher zero-shot performance of an object detection model. Furthermore, we demonstrated using photographs of virological plaque assays that gradient similarity affects performance on small object detection, which can be improved by mixing real and synthetic data. Finally, we turn procedural data rendering into an agentic skill (SynthClaw) to automate the procedural parameter optimisation.

Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as synthetic vision data generators? In this work, we revisit the promise of synthetic data as a scalable substitute for real training sets and uncover a surprising performance regression. We generate large-scale synthetic datasets using state-of-the-art T2I models released between 2022 and 2025, train standard classifiers solely on this synthetic data, and evaluate them on real test data. Despite observable advances in visual fidelity and prompt adherence, classification accuracy on real test data consistently declines with newer T2I models as training data generators. Our analysis reveals a hidden trend: These models collapse to a narrow, aesthetic-centric distribution that undermines diversity and real data distribution coverage. Overall, our findings challenge a growing assumption in vision research, namely that progress in generative realism implies progress in data realism. We thus highlight an urgent need to rethink the capabilities of modern T2I models as reliable training data generators.