| # Inference examples |
|
|
| This directory contains 40 numbered, independent inference examples. |
| Every example uses only its public number; source case names and internal paths |
| are intentionally omitted. |
|
|
| Each numbered directory contains: |
|
|
| - `first_frame.png`: exact 1536x864 generated RGB first frame used by inference. |
| - `prompts/*.txt`: the exact rolling long-inference prompts used for the result. |
| - `condition/*.npz`: ordered lossless condition chunks. |
| - `metadata.json`: frame count, FPS, prompt windows, and condition chunk ranges. |
|
|
| Prompt files are numbered in playback order. The first rolling window spans 124 |
| frames; each later window reuses 34 overlap frames and contributes 90 retained |
| frames. `metadata.json` records the conditioning and retained half-open frame |
| ranges for every prompt. A negative conditioning start means that the published |
| clip begins partway through an original rolling window; only the recorded |
| retained range belongs to the numbered example. |
|
|
| Each NPZ chunk has two arrays with the same `[T, 192, 336]` shape. The exact |
| historical Depth representation is declared by `depth_format` in each example's |
| `metadata.json`: |
|
|
| - `depth`: either little-endian `float32` metric first-surface depth in metres, |
| or little-endian `uint16` fixed-log Gray16 presentation codes. |
| - `semantic_id`: `uint8`, exact class IDs in `[0, 11]`. |
|
|
| For fixed-log examples, `condition_preprocessing` records the exact mapping and |
| spatial resampling contracts used by inference. Depth is mapped at 672x384 and |
| then reduced with an integer 2x2 BOX mean; Semantic-ID uses a categorical 2x2 |
| mode with the nearest-center tie break. No interpolation is applied to IDs. |
|
|
| The data runs at 24 FPS. Chunks are consecutive and must be concatenated in |
| filename order. They are storage chunks only and introduce no dropped or |
| duplicated frames. |
|
|
| ```python |
| from pathlib import Path |
| import numpy as np |
| |
| example = Path("1") |
| parts = [np.load(path, allow_pickle=False) for path in sorted((example / "condition").glob("*.npz"))] |
| depth = np.concatenate([part["depth"] for part in parts], axis=0) |
| semantic_id = np.concatenate([part["semantic_id"] for part in parts], axis=0) |
| for part in parts: |
| part.close() |
| ``` |
|
|
| `semantic_id_palette.json` documents the class meanings and provides an |
| optional visualization palette. The stored IDs themselves are not palette |
| colors and have not been passed through a lossy video codec. |
|
|
