---
pretty_name: Blender6D Track4D v3 BF16 preprocessed conditions
language:
- en
size_categories:
- 10K<n<100K
tags:
- blender6d
- synthetic
- video
- 3d
- world-model
- precomputed-latents
- bf16
---

# Blender6D Track4D v3 conditions — BF16 storage

Preprocessed conditioning data for **5,970 synthetic scenes and 23,880 directed
camera pairs**. Each scene supplies `cam_00 → cam_01`, `cam_01 → cam_02`,
`cam_02 → cam_03`, and `cam_03 → cam_00` at the same times.

## BF16 revision, 2026-10-24

Memory and control latents are now stored as **BF16 with lossless Zstandard
compression**, using `bf16_dynamic_zstd_v1`. Camera and geometry dtypes are preserved.

**These files were converted from the prior INT8 release.** The conversion stores
exactly the BF16 values returned by the previous loader, verified bit-for-bit for
every tensor. It does **not** recover precision lost during the original INT8
quantization and is **not** a fresh full-precision inference run. Per-sample
`precision_provenance` records `origin=dequantized_int8`, the prior quantization
errors, and `original_precision_recovered=false`. Source RGB, geometry, cameras,
poses, captions, pair identities, and frame selection are unchanged.

The previous INT8 publication remains available at revision
`f3f92e5cf02a19b48a89bb58a7b4ee91bfc36ffe`.

## Contents

- 33 target RGB frame indices at 30 fps; input resolution 704 × 1280.
- Five latent anchors; BF16 memory shape `[128, 5, 22, 40]` and control shape
  `[2, 128, 5, 22, 40]`, with SANA normalization.
- English captions, camera information, target indices, condition-image references,
  quality reports, object poses, and per-pair provenance.
- Metric geometry in metres: foreground depth uses a 0.001 conversion from source
  millimetres; background geometry comes from the source Blender scene.
- All detected dynamic regions are excluded before static-memory construction.
- Cross-camera identity conditions; no bank-shuffle variants for Blender6D.

**Raw RGB videos, original `.blend` files, external target latents, and text
embeddings are not included.** Those inputs must be supplied separately when
required by training. SpatialVID and Stereo4D outputs are separate datasets.

## Files

`shards/train-00000.tar` through `shards/train-00046.tar` contain checksum-verified
per-pair `conditions.zip`, `pairs-0000.jsonl`, `receipt.json`, and
`bf16-storage.json` under `data/scene_XXXXXX__cam_XX-cam_XX/`.

The top-level `pairs.jsonl` is portable: archive paths are relative to the download
root and RGB references use a `source-videos/` placeholder. Per-pair manifests and
archive metadata preserve original producer paths as provenance. Use the top-level
manifest after resolving paths for your server.

`checksums.json` records SHA-256 and byte counts for the publication files.
`provenance/` contains the preprocessing configuration, completion audit, and BF16
migration summary. Training checkpoints, execution logs, credentials, and rollback
copies are excluded.

## Download and prepare

```python
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id="HugMinjun/Blender6D-Track4D-v3",
    repo_type="dataset",
    local_dir="blender6d-track4d-v3-bf16",
)
```

Use Python 3.12+, or a Python 3.11 release supporting tar extraction filters:

```bash
python blender6d-track4d-v3-bf16/prepare_manifest.py \
  --root blender6d-track4d-v3-bf16 \
  --video-root /path/to/train_geometry_rgb_instance_motion_gmos_v2 \
  --verify-shards
```

The RGB root must contain `scene_XXXXXX/cameras/cam_XX/rgb.mkv`. The preparation
script extracts the archives, resolves paths, and writes `pairs.local.jsonl`.
It reports missing RGB videos without providing those videos.

Use a Track4D loader that recognizes `bf16_dynamic_zstd_v1`. In the prior
`training_preprocessing/compact.py`, add this identifier to `READABLE_FORMATS`;
the existing decoder already leaves BF16 tensors without `_qscale_` unchanged.
The included `bf16_loader.py` can also inspect individual samples independently:

```python
import json
from bf16_loader import load_conditions

with open("pairs.local.jsonl") as f:
    row = json.loads(next(f))
sample = load_conditions(row["archive"], row["sample"])
assert str(sample["memory_latent"].dtype) == "torch.bfloat16"
```

This minimal reader requires `torch`, `safetensors`, and `zstandard`; it does not
load RGB or create training targets. The initial condition latent must come from
the external target's exact first latent according to `external_target_contract`.

<!-- static-memory-holefill-20261025:start -->
## Static memory correction (2026-10-25)

Use the [static memory correction folder](holefill-fix-20261025/README.md) with both the
original cross-camera release and `temporal-same-camera-33/`. It repairs
6,187 affected condition archives while preserving all original downloads.
The corrected static hole-fill pyramid can materially change affected memory
latents. Other condition tensors, targets, captions and RGB references are unchanged.

Download `holefill-fix-20261025/` and run its `apply_patch_manifest.py` on each base release's
prepared local manifest. Use the resulting corrected manifest for training;
do not concatenate old and corrected manifests. The correction contains only
replacement archives, so existing base downloads can be reused.
<!-- static-memory-holefill-20261025:end -->