Optical flow estimation models are currently trained and evaluated on synthetic datasets. However, the generalizability of these models to real-world applications remains unexplored. This study investigates how well two state-of-the-art optical flow estimation models perform on real-world Articulated, Homothetic, and Conformal non-rigid motion. To facilitate evaluation, a manually annotated dataset comprising twenty-four real-world image pairs and sparse vector fields was created. Both models demonstrated performance consistent with synthetic benchmarks on Homothetic and Conformal motion. However, results degraded when evaluating Articulated motion, revealing limitations in real-world applicability for practical applications such as controlled robotics and object tracking.