| # EventActivityNet v1.0 Dataset Generation |
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| ## Source Video Lineage |
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| EventActivityNet v1.0 was generated from the non-interpolated/original-rate ActivityNet video lineage. It was not generated from the recovered 240fps interpolated video directory. |
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| Source FPS varies by video. The generator records FPS metadata per HDF5 file and preserves source spatial resolution. |
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| ## Large Subset Curation |
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| The Large release exactly matches the recovered original Large subset manifest. It contains 3,263 unique video IDs. |
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| The recovered subset curation implementation: |
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| - merges ActivityNet Captions train and validation metadata before sampling; |
| - uses seed `2025`; |
| - performs initial class-balanced sampling with `max(5, int(class_ratio * class_count))`; |
| - uses default `class_ratio=0.2`; |
| - length-balances using 33% and 66% duration quantiles; |
| - enriches with event-friendly examples from caption keywords or first-frame darkness. |
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| Event-friendly keywords: |
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| ```text |
| run, fast, sprint, night, dark, slow-motion |
| ``` |
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| Darkness threshold: |
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| ```text |
| normalized first-frame mean brightness < 0.4 |
| ``` |
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| The minimum-per-class rule is an initial sampling-stage rule. Later global length balancing can reduce final per-class counts below that initial quota. |
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| ## HDF5 Generation Pipeline |
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| The confirmed generation call chain is: |
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| - `activitynet.sh` |
| - `activitynet.py` |
| - `mp4_to_h5.mp4_to_h5_stream()` |
| - `data/v2v_core_esim_gpu.EventEmulatorGPU.video_to_voxel()` |
| - HDF5 writer |
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| Confirmed generation parameters: |
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| | Parameter | Value | |
| |---|---| |
| | Source video lineage | non-interpolated/original-rate ActivityNet videos | |
| | Number of bins | 5 | |
| | `frames_per_bin` | 1 | |
| | Spatial resizing | none; source resolution preserved | |
| | Output event dtype | `int16` | |
| | `voxel_event_start` dtype | `int64` | |
| | `voxel_event_count` dtype | `int32` | |
| | `events` compression | gzip+shuffle | |
| | Auxiliary compression | LZF+shuffle | |
| | Learned checkpoint required | no | |
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| The recovered implementation shows that no learned HyperE2VID/V2V checkpoint is used for HDF5 generation. Checkpoints found in the recovered project belong to downstream reconstruction/evaluation code, not to the generation of the HDF5 voxel files. |
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| ## Voxel Metadata Semantics |
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| The generator emits event slices from adjacent grayscale video frames and accumulates them into output voxel samples. |
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| - `voxel_event_start[t]` is the generated-slice start index for voxel sample `t`. |
| - `voxel_event_count[t]` is the number of generated slices accumulated into voxel sample `t`. |
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| ## Release Scale Construction |
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| Large is the recovered historical subset. |
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| Medium and Small are newly generated deterministic nested v1.0 release scales: |
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| ```text |
| Small subset Medium subset Large |
| ``` |
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| Target durations: |
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| - Medium: approximately 50 hours. |
| - Small: approximately 20 hours. |
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| Scale construction uses seed `2025` and stratifies by: |
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| ```text |
| (split, class_label, duration_bucket, event_friendly) |
| ``` |
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| Within each stratum, records are ranked deterministically by a stable hash of seed and video ID. |
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| ## Verified Output |
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| Final validation confirmed: |
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| - 3,263 valid HDF5 files; |
| - all files open successfully; |
| - zero truncated or unreadable files; |
| - zero remaining structural warnings; |
| - Large train/validation split: 2,316 / 947. |
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