Two access tracks (see LICENSE.md). Internal Evaluation License: 90-day
internal evaluation, no publication or release obligation, no deployment or production use; delete or
convert at the end. Full dataset / commercial: production training and deployment rights per agreement
with Origin Lab. No redistribution of the data in any form.

Origin Lab Game Scenes: Posed RGB-D Flythroughs of Game Worlds

Every frame carries the camera that rendered it and the depth the engine computed for it. Ten game worlds, with the camera released from the player for 60% of the footage: metric depth, world-space normals, 4x4 pose, and per-frame intrinsics on one frame index, plus hundreds of full in-place turns and long stretches in which the world is frozen and only the camera moves. Two trajectories per world and one whole world are held out, so sparse-view reconstruction and camera-controlled generation can be scored against exact novel views rather than estimated ones. Nothing here was reconstructed after the fact.

For sparse-view 3D reconstruction, novel view synthesis, camera-conditioned world models, and real-to-sim scene priors. Static spans are flagged; dynamic-object masks ship with the next modality drop.

Version 1.0.0

One session, four synchronized streams as quadrants of a single decoded video: clean RGB, post-HUD RGB, world-space normals, and depth. The camera is off the character, circling a building at walking height while the world holds still.

The same frames, twice: the clean RGB on the left, the engine's depth on the right. One decode, one frame index, no alignment step. The balconies read as overhangs, the roof line separates from the trees behind it, and the sky returns nothing because the engine had no surface to report.

	Published here	Full corpus
Sessions	21	186
Hours	18.6	166
Worlds	10, anonymized as Game 1 to Game 10	10
Size	599 GB	about 5 TB

What you can download here is a sample. This repository carries 21 complete sessions, 599 GB of them, every stream intact, plus 61 clips cut from their flythrough spans and the metadata for the set. Those 21 sessions are themselves drawn from a corpus of 186 sessions and 166 hours that cleared the same quality gates, and every distribution below is measured across that full corpus. To request the full dataset, write to hello@originlab.ai.


Capture	1920x1080, 60 FPS constant, in-engine SDK, one shared frame clock
Camera pose	read out of the engine, present on 98.9% of frames
Depth	metric, engine G-buffer, never re-encoded
Delivery	whole sessions, no archives to unpack

Dataset summary

This is game footage recorded from inside the engine with the camera detached from the player. An operator flies the camera through the world while the simulation is frozen, then hands it back; the recorder marks every span the camera is off the character. What ships is not a reconstruction of that flight but its ground truth: the pose the engine used to render each frame, the depth buffer it computed, the surface normals it shaded with, and the intrinsics it projected through, all on the same frame index as the pixels.

Two properties make it usable as supervision rather than as reference footage. Alignment: every stream in a session starts at frame 0 and frame k matches frame k, so the frame index is the only join key required. Ground truth, not estimation: no pose was solved, no scale was fitted, and no depth was inferred from pixels, which is what lets a reconstruction or a camera-conditioned generator be scored against this data rather than merely trained on it.

Supported tasks

Task	Why this dataset
Sparse-view 3D reconstruction	Exact per-frame extrinsics and intrinsics with metric depth, and a held-out split of whole trajectories and one whole world for scoring against real novel views.
Novel view synthesis	Released-camera frames sit a typical 94.2 m from the character, with 92% past 10 m: viewpoints and baselines a third-person rig never produces.
Camera-conditioned generation and world models	Dense posed frames over long continuous flights, hour-long sessions with the camera off the character for most of their length.
Monocular and metric depth	Engine G-buffer depth on every frame, never re-encoded, with the near plane and intrinsics written per session in the decode contract.
Static-scene capture	Spans in which the simulation is frozen and only the camera moves, flagged span by span in `metadata/static_spans.parquet` (11.0 hours in the published sample).

What the camera does

Where the camera sits, frame by frame. On the character it stays inside a third-person rig at a typical 1.5 m; released it sits at a typical 94.2 m, and 92% of released frames are past 10 m, further than any third-person camera reaches. That gap is the dataset: viewpoints a player's camera never occupies.


_{What the footage is of. 69% of frames are outdoors, spread across desert, forest, alpine, grassland, urban, industrial, cave and interior, with no single terrain past a fifth of the corpus.}	_{Lighting and weather, over outdoor frames only so indoor scenes are not miscounted as night: 59.6% daylight, with night, fog, overcast, rain and dusk all represented.}

What the depth and the poses rebuild together

A full 359 degree turn in place, fused into one surface. Every depth sample is placed in the world with the pose the engine recorded for its own frame and integrated into a signed distance field; a surface is drawn only where several frames agree. No registration, no bundle adjustment, no scale fitting, no model in the loop. Vaulted ceilings, door frames, window reveals and furniture are what the recorded geometry already contains.

The footage it was built from: clean RGB on the left, the engine's depth for the same frames on the right.

This reconstruction comes from an earlier capture run on the same pipeline. The published sample alone holds 415 full in-place turns across 20 of its 21 sessions, listed per session in metadata/scenes.parquet; the full corpus holds proportionally more, and fused panoramas ship to access holders.

Composition of the published sample

Measured per session over the delivered pose tracks, and reported here so the sample can be checked against the corpus distributions above. metadata/scenes.parquet holds the full table.

	Across the 21 published sessions
Median session	60.0 minutes, unbroken
Flythrough share	60% of footage, every session releases the camera
Frozen-world footage	11.0 hours, flagged as 260 spans in `metadata/static_spans.parquet`
Full in-place turns	415, across 20 of the 21 sessions
Pose coverage	98.9% of frames, never below 94.8% in a session
Enclosure	9 sessions open sky, 7 enclosed, 4 crossing between the two

Scene identity is anonymized: map_name in metadata/scenes.parquet carries the engine's own level name where the capture exposes one (15 of 21 sessions), which separates distinct worlds within a title without naming the title.

Dataset structure

One folder per session, grouped by modality, no archives to unpack. A one-hour session measures about 30 GB: roughly 18 GB of video renditions, 2.3 GB of depth, and 200 MB of telemetry and tables.

File	Purpose
`video/prehud.mp4`	1080p H.264, 60 FPS CFR, HUD removed, with game audio
`video/posthud.mp4`	The frame as the player saw it, HUD included, same clock
`video/normals.mp4`	Per-pixel surface orientation in the world frame
`video/mosaic.mp4`	The four visual streams as quadrants of one video, a playable sync proof
`depth/depth.hevc`	10-bit HEVC elementary stream, log-encoded planar Z; frame k is RGB frame k
`depth/depth_meta.jsonl`	Per-frame depth parameters
`depth/decode_contract.json`	Near plane (makes depth metric), pinhole intrinsics, frame accounting, world frame
`telemetry/camera.jsonl`	Camera pose by frame index and QPC, position and pitch/yaw/roll
`telemetry/input.jsonl`	Keyboard, mouse deltas, scroll, and window focus, frame-indexed
`telemetry/events.jsonl`	In-engine action events with the engine's own label, frame-indexed
`telemetry/state.jsonl`	Sampled game state, field, unit, and value per sample
`telemetry/world.json`	Engine, world-to-meters scale, handedness, gravity
`tables/frames.parquet`	One row per frame: pose, held-keys bitmask, mouse deltas, state columns, event flags
`tables/events.parquet`	One row per in-engine event with resolved labels
`session.json`	Manifest: files and sizes, fps, the shared-clock alignment statement, sync audit

Complete packages sit under sessions/<uuid>/, one folder per session with the file tree above. Alongside them: metadata/sessions.parquet (the session index the viewer renders), metadata/frames.parquet (4.0 M rows, every frame of the sample with pose and action columns), metadata/scenes.parquet, metadata/static_spans.parquet, metadata/splits.parquet, and previews/ (61 clips cut from flythrough spans). The remaining 165 sessions of the corpus are delivered on request.

Held-out split

metadata/splits.parquet marks two flythrough trajectories per world plus every session of one whole world as held out, so sparse-view reconstruction and camera-conditioned generation can be scored on views no training run saw.

Split	Rows	What it is
`train`	39	flythrough spans free to train on
`heldout_traj`	18	two trajectories per world, withheld
`heldout_title`	6	every span of one whole world, withheld

How to use it

from huggingface_hub import hf_hub_download, snapshot_download
import pyarrow.parquet as pq

REPO = "originlab/game-scenes-posed-rgbd"

# the session index, and the per-frame table for the whole sample
frames = pq.read_table(hf_hub_download(REPO, "metadata/frames.parquet", repo_type="dataset"))
print(frames.num_rows, frames.schema.names)   # pose and action columns, one row per frame

# one complete session, about 30 GB
snapshot_download(REPO, repo_type="dataset", local_dir="./game-scenes",
                  allow_patterns=["metadata/*", "sessions/<uuid>/*"])

Depth ships as a plain HEVC elementary stream read with ffmpeg as gray16le. Each session carries its own depth/decode_contract.json (intrinsics, near plane, transform constants) and depth/depth_meta.jsonl, which maps every depth frame to the camera frame it was captured against; use that mapping rather than assuming the two indices coincide. The decode reference for this release ships to access holders alongside the loader.

Baselines

Pointmap AbsRel and pose error on the held-out split, measured against VGGT, Pi3, MapAnything, and Depth Anything 3, are in preparation and ship to access holders.

Considerations for using the data

Flythrough spans are frozen-world capture: the simulation is paused, so the geometry in a static span does not move. Spans outside those flags are ordinary dynamic play; dynamic-object masks ship with optical flow and segmentation in the next modality drop.
Depth comes from the engine's depth buffer, so translucent effects such as fog, glass, and particles follow how the engine renders them.
Pose is present on 98.9% of frames across the release and on at least 94.8% in every session; cam_valid in the frames table marks the gaps rather than interpolating them.
Depth and normals ship as HEVC and MP4 lanes. Per-frame EXR conversion is lossless from the delivered stream through the decode contract and is available under agreement.
Object counts, material passes, and mesh exports are not part of this release. Depth-fused point clouds per scene and PBR passes where the engine exposes them are available under agreement.
Worlds are anonymized as Game N in the public metadata; real titles are disclosed under the full-dataset agreement.
Personal and sensitive information: audio is game audio from the title process, no microphone or player voice is captured, and no personally identifying information about the players ships in any stream or metadata file.

Requesting the full dataset

Write to hello@originlab.ai with what you need. What ships is considerably larger than what you can download here:

	Downloadable here	Full dataset
Sessions	21	186
Hours	18.6	166
Size	599 GB	about 5 TB
Worlds	10	10
Form	every session whole, all modalities	the same, across the corpus

Each full session is an unbroken hour at 1920x1080 and 60 FPS with every stream on one frame clock: clean RGB, post-HUD RGB, world-space normals, metric depth, per-frame camera pose and intrinsics, keyboard and mouse input, in-engine events and game state, the 4-up verification mosaic, and per-frame training tables. A one-hour session measures roughly 30 GB.

Useful things to say in the mail:

Which slice. By world, by scene type (open sky, enclosed, mixed), by camera behaviour (flythrough spans, frozen-world spans, in-place turns), or the whole corpus.
Which modalities. Take the full set or only what you train on; depth and pose alone are a fraction of the bytes.
How you want it. A signed download manifest for any parallel downloader, or direct in-cloud access on AWS for the fastest transfer.
Which access track. Internal evaluation or commercial, as set out below.

Delivery is by manifest rather than browser download: a signed list of every file in your selection, valid for seven days, resumable and multi-connection. A single session lands in minutes on a gigabit line, a large selection overnight.

Licensing

All gameplay is recorded under exclusive licenses with the rights holders and captured by consenting, compensated players. Access is gated; request the track you need in the access form:

Internal Evaluation License: 90-day internal evaluation, train and evaluate models solely to assess the data's value. No publication or release obligation, no deployment or production use. At the end of the period, delete the data and evaluation weights, or convert to a commercial agreement.
Full dataset / commercial: production training and deployment rights defined per agreement. Contact Origin Lab to license.

No redistribution of the data in any form. See LICENSE.md for the complete terms.

The full corpus, 186 sessions and 166 hours, is delivered once access is granted; the sample in this repository is there so the format and the quality can be checked first. Request it at hello@originlab.ai.

Citation

@misc{originlab2026gamescenes,
  title  = {Origin Lab Game Scenes: Posed RGB-D Flythroughs of Game Worlds},
  author = {Origin Lab},
  year   = {2026},
  url    = {https://huggingface.co/datasets/originlab/game-scenes-posed-rgbd}
}

Related datasets from Origin Lab

game-recordings-v3, frame-synced RGB-D gameplay with actions, events, and game state.
game-depth, RGB paired with the engine's dense depth buffer.
frame-synced-multiplayer, eight players on one frame clock.
lotus-game-depth, a depth model trained on Origin Lab data.

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