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id string | vae_latent_bytes unknown | vae_latent_shape list | vae_latent_dtype string | text_embedding_bytes unknown | text_embedding_shape list | text_embedding_dtype string | first_frame_latent_bytes unknown | first_frame_latent_shape list | first_frame_latent_dtype string | clip_feature_bytes unknown | clip_feature_shape list | clip_feature_dtype string | track_points_bytes unknown | track_points_shape list | track_points_dtype string | track_visibility_bytes unknown | track_visibility_shape list | track_visibility_dtype string | object_ids_bytes unknown | object_ids_shape list | object_ids_dtype string | track_weights_bytes unknown | track_weights_shape list | track_weights_dtype string | file_name string | caption string | media_type string | width int64 | height int64 | num_frames int64 | duration_sec float64 | fps float64 |
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End of preview.
OpenVid-WanTrack — preprocessed training data
Preprocessed (FastVideo parquet) data used to train the TrackWan point-track-conditioned video model. Derived from OpenVid; each row is one 121-frame clip with everything the trainer consumes, so it is self-contained (no re-encoding needed).
Contents (parquet columns)
Each field is stored as *_bytes / *_shape / *_dtype (numpy buffers):
vae_latent— Wan VAE latent of the clipfirst_frame_latent— I2V conditioning latent (frame 0)text_embedding— T5 caption embedding (text conditioning WAS used in training)clip_feature— CLIP image featurestrack_points,track_visibility,track_weights— CoTracker point tracksobject_ids— SAM segmentation IDs per track (object grouping / coverage)caption— raw text prompt; plusfile_name,width,height,num_frames,fps,duration_sec
Prompt list is also provided as videos2caption.json.
Layout
shard_XXXXX/worker_{0,1}/*.parquet — read the whole tree recursively as one dataset
(FastVideo's get_parquet_files_and_length walks it). ~1,501 shards, ~4,500 parquet files, ~5.5 TB.
License
Apache-2.0. Derived from OpenVid; respect the upstream OpenVid terms for the source videos.
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