README.md

metadata

license: other

multi3d_games Latent Dataset

Partial Multi3D games latent dataset uploaded to mignonjia/multi3d_games.

This dataset uses the expanded parquet layout rather than tar archive shards. It contains latent/video-conditioning rows for four gameplay videos from Horizon Forbidden West, Dark Souls Remastered, and Code Vein.

Layout

README.md
map_style_cache/file_info.pkl
action_latent/node1/manifest.jsonl
action_latent/node1/manifest.rankNNN.jsonl
action_latent/node1/dist_merged.ok
action_latent/node1/dist_done/rankNNN.done
action_latent/node1/combined_parquet_dataset/rankNNN/worker_0/*.parquet

The uploaded repo contains one node (node1) split across 8 ranks: rank000 through rank007.

Size and Counts

• Total uploaded data size: about 224.7 GB
• Uploaded files: 1,715 data files plus README.md and .gitattributes
• Parquet files: 1,696
• Parquet rows / samples: 13,551
• Manifest rows:
  • manifest.jsonl: 13,551 rows
  • rank manifests: 13,551 rows total
• Rank parquet layout:
  • rank000: 212 parquet files, 1,694 rows, 26.17 GiB
  • rank001: 212 parquet files, 1,694 rows, 26.13 GiB
  • rank002: 212 parquet files, 1,694 rows, 26.14 GiB
  • rank003: 212 parquet files, 1,694 rows, 26.16 GiB
  • rank004: 212 parquet files, 1,694 rows, 26.15 GiB
  • rank005: 212 parquet files, 1,694 rows, 26.17 GiB
  • rank006: 212 parquet files, 1,694 rows, 26.16 GiB
  • rank007: 212 parquet files, 1,693 rows, 26.16 GiB

Source Videos

The rows come from four source videos:

idx	video_id	game	rows	shard
2246	KchWtQyuyvU	Horizon Forbidden West	3,396	SHARD_0006
8224	d_lTaTapecI	Dark Souls Remastered	3,969	SHARD_0026
12601	m2Nt3DVfYqk	Code Vein	1,847	SHARD_0040
18529	soS-p5-Oh7A	Dark Souls Remastered	4,339	SHARD_0059

Parquet Schema

Each parquet row stores byte arrays plus explicit shape and dtype metadata. The main fields are:

• id: sample id, matching manifest ids
• vae_latent_bytes, vae_latent_shape, vae_latent_dtype
• clip_feature_bytes, clip_feature_shape, clip_feature_dtype
• first_frame_latent_bytes, first_frame_latent_shape, first_frame_latent_dtype
• mouse_cond_bytes, mouse_cond_shape, mouse_cond_dtype
• keyboard_cond_bytes, keyboard_cond_shape, keyboard_cond_dtype
• pil_image_bytes, pil_image_shape, pil_image_dtype
• file_name, caption, media_type, width, height, num_frames, duration_sec, fps

Example row metadata:

• vae_latent_shape: [16, 21, 60, 104], dtype float32
• first_frame_latent_shape: [16, 21, 60, 104], dtype float32
• clip_feature_shape: [257, 1280], dtype float32
• mouse_cond_shape: [81, 2], dtype float32
• keyboard_cond_shape: [81, 6], dtype float32
• media_type: video
• width: 480
• height: 832
• num_frames: 21
• duration_sec: 2.7
• fps: 30.0

Processing Notes

• Source local root before upload: /mnt/weka/home/hao.zhang/alex/wm-lab/datas/datasets/multi3d-partial
• The dataset was uploaded directly with hf upload-large-folder, preserving the expanded parquet paths.
• The original local upload command used 8 workers and committed all 1,715 files successfully.
• Multi3D mouse up/down convention was corrected before upload by flipping mouse_cond[:, 0].
• The mouse flip was validated over all 1,696 parquet files and 13,551 rows. The final scan showed the expected swapped axis-0 sign counts relative to the pre-flip baseline.

Download

Download the full dataset:

hf download mignonjia/multi3d_games --repo-type dataset --local-dir multi3d_games

Download one rank only:

hf download mignonjia/multi3d_games \
  --repo-type dataset \
  --include 'action_latent/node1/combined_parquet_dataset/rank000/**' \
  --local-dir multi3d_games_rank000

Reading Arrays

The array fields are stored as raw bytes. Reconstruct them using the matching *_shape and *_dtype columns:

import numpy as np
import pandas as pd

df = pd.read_parquet("action_latent/node1/combined_parquet_dataset/rank000/worker_0/data_chunk_0.parquet")
row = df.iloc[0]

vae = np.frombuffer(row["vae_latent_bytes"], dtype=np.dtype(row["vae_latent_dtype"]))
vae = vae.reshape(tuple(row["vae_latent_shape"]))

mouse = np.frombuffer(row["mouse_cond_bytes"], dtype=np.dtype(row["mouse_cond_dtype"]))
mouse = mouse.reshape(tuple(row["mouse_cond_shape"]))

keyboard = np.frombuffer(row["keyboard_cond_bytes"], dtype=np.dtype(row["keyboard_cond_dtype"]))
keyboard = keyboard.reshape(tuple(row["keyboard_cond_shape"]))

Verification

• Hugging Face repo after upload contained 1,716 files: .gitattributes plus 1,715 uploaded dataset files.
• Upload log final state:
  • hashed: 1,715 / 1,715
  • pre-uploaded: 1,697 / 1,697
  • committed: 1,715 / 1,715
  • committed bytes: 224.7 GB / 224.7 GB