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Dataset for the 1X World Model Challenge.

Download with:

huggingface-cli download 1x-technologies/worldmodel --repo-type dataset --local-dir data

Changes from v1.1:

Contents of train/val_v2.0:

The training dataset is shareded into 100 independent shards. The definitions are as follows:

  • video_{shard}.bin: 8x8x8 image patches at 30hz, with 17 frame temporal window, encoded using NVIDIA Cosmos Tokenizer "Cosmos-Tokenizer-DV8x8x8".

  • segment_idx_{shard}.bin - Maps each frame i to its corresponding segment index. You may want to use this to separate non-contiguous frames from different videos (transitions).

  • states_{shard}.bin - States arrays (defined below in Index-to-State Mapping) stored in np.float32 format. For frame i, the corresponding state is represented by states_{shard}[i].

  • metadata - The metadata.json file provides high-level information about the entire dataset, while metadata_{shard}.json files contain specific details for each shard.

    Index-to-State Mapping (NEW)

     {
          0: HIP_YAW
          1: HIP_ROLL
          2: HIP_PITCH
          3: KNEE_PITCH
          4: ANKLE_ROLL
          5: ANKLE_PITCH
          6: LEFT_SHOULDER_PITCH
          7: LEFT_SHOULDER_ROLL
          8: LEFT_SHOULDER_YAW
          9: LEFT_ELBOW_PITCH
          10: LEFT_ELBOW_YAW
          11: LEFT_WRIST_PITCH
          12: LEFT_WRIST_ROLL
          13: RIGHT_SHOULDER_PITCH
          14: RIGHT_SHOULDER_ROLL
          15: RIGHT_SHOULDER_YAW
          16: RIGHT_ELBOW_PITCH
          17: RIGHT_ELBOW_YAW
          18: RIGHT_WRIST_PITCH
          19: RIGHT_WRIST_ROLL
          20: NECK_PITCH
          21: Left hand closure state (0 = open, 1 = closed)
          22: Right hand closure state (0 = open, 1 = closed)
          23: Linear Velocity
          24: Angular Velocity
      }
    

Previous version: v1.1

  • magvit2.ckpt - weights for MAGVIT2 image tokenizer we used. We provide the encoder (tokenizer) and decoder (de-tokenizer) weights.

Contents of train/val_v1.1:

  • video.bin - 16x16 image patches at 30hz, each patch is vector-quantized into 2^18 possible integer values. These can be decoded into 256x256 RGB images using the provided magvig2.ckpt weights.
  • segment_ids.bin - for each frame segment_ids[i] uniquely points to the segment index that frame i came from. You may want to use this to separate non-contiguous frames from different videos (transitions).
  • actions/ - a folder of action arrays stored in np.float32 format. For frame i, the corresponding action is given by joint_pos[i], driving_command[i], neck_desired[i], and so on. The shapes and definitions of the arrays are as follows (N is the number of frames):
    • joint_pos (N, 21): Joint positions. See Index-to-Joint Mapping below.
    • driving_command (N, 2): Linear and angular velocities.
    • neck_desired (N, 1): Desired neck pitch.
    • l_hand_closure (N, 1): Left hand closure state (0 = open, 1 = closed).
    • r_hand_closure (N, 1): Right hand closure state (0 = open, 1 = closed).

    Index-to-Joint Mapping (OLD)

     {
          0: HIP_YAW
          1: HIP_ROLL
          2: HIP_PITCH
          3: KNEE_PITCH
          4: ANKLE_ROLL
          5: ANKLE_PITCH
          6: LEFT_SHOULDER_PITCH
          7: LEFT_SHOULDER_ROLL
          8: LEFT_SHOULDER_YAW
          9: LEFT_ELBOW_PITCH
          10: LEFT_ELBOW_YAW
          11: LEFT_WRIST_PITCH
          12: LEFT_WRIST_ROLL
          13: RIGHT_SHOULDER_PITCH
          14: RIGHT_SHOULDER_ROLL
          15: RIGHT_SHOULDER_YAW
          16: RIGHT_ELBOW_PITCH
          17: RIGHT_ELBOW_YAW
          18: RIGHT_WRIST_PITCH
          19: RIGHT_WRIST_ROLL
          20: NECK_PITCH
      }
    

We also provide a small val_v1.1 data split containing held-out examples not seen in the training set, in case you want to try evaluating your model on held-out frames.

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