NameFrame/winter-unreal-synthetic

Russian winter town: a synthetic detection capture rendered in Unreal Engine

300 rendered frames from a single Unreal Engine environment, carrying 7,794 labelled object instances. Every box and every mask here is read out of the engine's own per-instance ID buffer at render time. No model produced these labels and no one drew them by hand, so a label is wrong only where the scene description behind it is wrong.

The capture

Engine	5.8.1-56057345+++UE5+Release-5.8
Map	RussianWinterTownDemo01
Frames	300
Instances	7,794
Resolution	1280 x 960
Horizontal FOV	57.3 deg
Camera policy	camera_zones_look_at_target, 8 authored zones
Lens profile	realistic_drone at 0.6
Time of day	07:00 to 19:00
Weather	scene (300 frames)
Master seed	20260820
Validator	grade A, 93.3 out of 100

Placement, pose and camera pose are sampled deterministically from the master seed (deterministic_per_logical_frame_and_instance), so the same seed over the same scene reproduces the same 300 frames.

Classes

id	class	instances
0	person	1,870
1	barrel	912
2	box	816
3	crate	867
4	antenna	504
5	tire	612
6	pallet	538
7	bucket	498
8	cone	493
9	bench	383
10	car	301

By COCO's scale thresholds: 4,442 small, 2,973 medium, 379 large. The mean box covers 0.18% of the frame, which makes this a small-object capture.

Layout

images/          rendered RGB frames, unmodified PNG
segmentation/    per-instance ID buffers, one colour per instance
labels/          YOLO boxes, one text file per frame
annotations/     the same labels in COCO format
metadata/        per-frame camera, actor and spawn-manifest records
reports/         this capture's own report and validation output
capture.json     the run contract: classes, camera policy, seed
data.yaml        class names, ready for a YOLO trainer

Frame N is the same scene across images/, segmentation/, labels/ and metadata/. No split is imposed: all 300 frames sit in one pool, so you can split them however the experiment needs.

Decode segmentation/*.png with a library that hands you channels in the order the file declares. The instance colours are exact 8-bit values, and a silent channel swap turns a correct mask lookup into an empty one.

Measured quality

This archive ships the validator's own output rather than a summary of it. From reports/validation.json:

• Corrupt images: 0 of 300
• Frames with no labels: 0
• Near-duplicate images: 0
• Cross-split leakage: 0
• Mean luminance 109.8; 3 underexposed frames, 0 overexposed
• Reconciled frame by frame: 300 requested, 300 images, 300 label files, 7,794 annotations

Known limitations

• The validator took 6.71 points off for class-imbalance: rarest/commonest class ratio 0.161.
• Depth-probed people the engine could see, against people labelled: 1/1795 engine-visible people unlabelled (0.06%, 300/300 frames).
• Camera placements are drawn from 8 authored zones under the camera_zones_look_at_target policy, so viewing angles and distances cover a narrow band. This is a sample of one environment, not a benchmark.
• Rendered imagery carries a synthetic-to-real domain gap. Treat it as pre-training or augmentation material rather than a substitute for a real-world evaluation set.

Provenance and terms

The frames were rendered in Unreal Engine and labelled from the engine's instance buffers by NameFrameCapture, an editor plugin. capture.json carries the full build record, including the plugin and job hashes and the checks the build could not verify.

Environment and prop assets in the scene are licensed from third parties for use inside Unreal Engine projects. The rendered images and their labels are published here; the source assets are not, and no .uasset or map file is included. Read LICENSE before redistributing the imagery itself.

The capture pipeline, the other environments in this series and the measurements behind them are documented at https://getnameframe.com.