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NagaYu/cairn-departure-return

cairn-departure-return Departure -> return trajectories with exact ground truth, for measuring whether a video world model keeps a world when the camera looks away. Each episode: a room of well-separated but deliberately confusable objects (two near-duplicate colour pairs, repeated shapes), and a camera that observes a target object, turns away for a measured number of frames, and comes back from a different viewpoint -- so a model cannot pass by replaying its last frame. A… See the full description on the dataset page: https://huggingface.co/datasets/NagaYu/cairn-departure-return.

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cairn-departure-return

Departure -> return trajectories with exact ground truth, for measuring whether a video world model keeps a world when the camera looks away.

Each episode: a room of well-separated but deliberately confusable objects (two near-duplicate colour pairs, repeated shapes), and a camera that observes a target object, turns away for a measured number of frames, and comes back from a different viewpoint -- so a model cannot pass by replaying its last frame.

A third of the episodes carry a world edit (move, remove, set_attr) scheduled to fire while the target is off screen, for measuring instruction compliance on objects the model cannot see at the moment of the command.

Fields

field	meaning
`episode_id`	stable identifier
`seed`, `n_objects`	scene construction parameters
`objects`	ground truth per object: id, name, position, yaw, 8-D appearance
`cameras`	per-frame eye position, yaw, pitch, horizontal fov
`visibility`	`(frames x objects)` boolean, rasterised, occlusion included
`target_id`	the object the camera leaves and returns to
`departure_frame`, `return_frame`, `absence_frames`	measured, not requested
`edit`	scheduled edit, or `null`

Metrics

Use cairn.metrics and cairn.runner.run_condition, or score your own model: compare the target's pose/appearance in the frames after return_frame against the frames before departure_frame, and report the result as a function of absence_frames. A method with implicit memory will show a curve that degrades with the x-axis; that degradation is the point of the dataset.

Generated by `scripts/build_bench.py` in the Cairn repository. Interactive demo: NagaYu/cairn.