Event data is the starting point for most process mining methods. In traditional process mining, events are assumed to belong to a single case identifier. Recently, concepts emerged that lift this assumption and extend the information captured in event data. Two of them are: object-centric process mining and translucent process mining. In object-centric process mining, events can be connected to multiple objects from different object types. This allows for capturing inter-object dependencies that are present in most real-world processes. In translucent process mining, each event also contains information on which activities could have been performed—besides the executed activity. Both approaches have proven to improve process mining tasks, such as process discovery and conformance checking. So far, they have only been investigated in isolation. In this paper, we propose a way to combine object-centric and translucent process mining. We provide a formalization for event data that is both object-centric and translucent. Also, we propose a research agenda for further investigating object-centric translucent process mining.