Applying the proposed methods from the HIMO dataset to real-world scenarios presents several challenges, particularly in uncontrolled environments. One significant challenge is the variability in lighting conditions, which can affect the performance of motion capture systems and the accuracy of object tracking. In uncontrolled settings, shadows and reflections may introduce noise, complicating the extraction of precise motion data.
Another challenge is the presence of occlusions, where objects or body parts may be blocked from view by other objects or individuals. The HIMO dataset employs a hybrid MoCap system to mitigate occlusions, but in real-world applications, the dynamic nature of environments can lead to unpredictable occlusions that the system may not be able to handle effectively. This could result in incomplete or inaccurate motion data, affecting the realism and plausibility of the generated human-object interactions.
Additionally, the complexity of human behavior in real-world scenarios, including spontaneous actions and interactions with multiple objects simultaneously, may not be fully captured by the structured sequences in the HIMO dataset. This variability necessitates robust models capable of adapting to unforeseen circumstances and generating coherent interactions despite the lack of controlled conditions.