A Hybrid Approach for Closing the Sim2real Appearance Gap in Game Engine Synthetic Datasets
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| Authors | Stefanos Pasios |
| Year | 2026 |
| HF Upvotes | 1 |
| arXiv | 2605.02291 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Video game engines have been an important source for generating large volumes of visual synthetic datasets for training and evaluating computer vision algorithms that are to be deployed in the real world. While the visual fidelity of modern game engines has been significantly improved with technologies such as ray-tracing, a notable sim2real appearance gap between the synthetic and the real-world images still remains, which limits the utilization of synthetic datasets in real-world applications. In this letter, we investigate the ability of a state-of-the-art image generation and editing diffusion model (FLUX.2-4B Klein) to enhance the photorealism of synthetic datasets and compare its performance against a traditional image-to-image translation model (REGEN). Furthermore, we propose a hybrid approach that combines the strong geometry and material transformations of diffusion-based methods with the distribution-matching capabilities of image-to-image translation techniques. Through experiments, it is demonstrated that REGEN outperforms FLUX.2-4B Klein and that by combining both FLUX.2-4B Klein and REGEN models, better visual realism can be achieved compared to using each model individually, while maintaining semantic consistency. The code is available at: https://github.com/stefanos50/Hybrid-Sim2Real
Engineering Breakdown
Plain English
This paper addresses the sim2real gap in synthetic datasets generated by game engines by comparing a modern diffusion model (FLUX.2-4B Klein) against traditional image translation methods to improve photorealism. The researchers evaluate whether these models can effectively bridge the visual fidelity gap between game engine renders and real-world images, enabling better use of synthetic training data in production systems.
Key Engineering Insight
Diffusion-based image generation models appear to outperform traditional image-to-image translation approaches for closing the sim2real gap, suggesting that modern generative approaches are more effective than previous domain adaptation techniques at handling the complex visual distribution shift between synthetic and real data.
Why It Matters for Engineers
Engineers building computer vision systems often rely on synthetic data because it's cheaper and easier to generate at scale than collecting labeled real-world images. If diffusion models can reliably close the appearance gap, teams can train on pure synthetic datasets without expensive real-world data collection or complex domain adaptation pipelines, directly reducing time-to-production and data annotation costs.
Research Context
Game engines have been used for years to generate training data, but the visual domain gap consistently degraded real-world performance. Previous solutions used traditional style transfer or domain adaptation techniques with limited success. This work advances the state by leveraging modern generative AI (specifically diffusion models) to post-process synthetic datasets, offering a practical pathway to make synthetic-only training viable for vision applications.
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