Not aware of this type of simplistic embeddings. I think getting the first few layers of a large pretrained vision model will get you better results. Blindly learning from a generic image would probably steer it toward the dominant textures and shapes rather than linear operation of camera moves.

The simplest embeddings for vision should focus on camera primitives and invariants. Translation, rotation, scale, skew, projections, lighting. It doesn't matter that much what you use in the layers, but you should steer the training with augmented data. Like rotate and skew the objects in the batches to make sure the layers are invariant to these things.

Next are some depth-mapping embeddings which go beyond flat camera awareness.

The best papers I've seen are face embeddings. You can get useful results with smaller models. There are of course deeper embeddings that focus on the whole scene and depth maps but those are huge.