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README.md

@@ -79,6 +79,12 @@ Export to ONNX (modify paths if needed):
 python utils/pth_to_onnx.py
 ```
 
+# Research
+
+Synthetic datasets have limitations for achieving great segmentation results. This is because artificial lighting, occlusion, scale or backgrounds create a gap between synthetic and real images. A "model trained solely on synthetic data generated with naïve domain randomization struggles to generalize on the real domain", see [PEOPLESANSPEOPLE: A Synthetic Data Generator for Human-Centric Computer Vision (2022)](https://arxiv.org/pdf/2112.09290). However, hybrid training approaches seem to be promising and can even improve segmentation results.
+
+Currently I am doing research how to close this gap with the resources I have. There are approaches like considering the pose of humans for improving segmentation results, see [Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation (2019)](https://arxiv.org/pdf/1907.05193).
+
 ## Support
 
 This is the first iteration of the model, so there will be improvements!