Datasets:
Languages:
English
Size:
n<1K
ArXiv:
Tags:
motion-generation
co-speech-gesture-generation
streaming-generation
beat2
smpl-x
arxiv:2608.01643
License:
File size: 5,036 Bytes
44e8883 06dff65 44e8883 06dff65 44e8883 06dff65 44e8883 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | ---
license: other
pretty_name: StreamTalk Inference Data
language:
- en
tags:
- motion-generation
- co-speech-gesture-generation
- streaming-generation
- beat2
- smpl-x
- arxiv:2608.01643
size_categories:
- n<1K
---
# StreamTalk Inference Data
Ready-to-compare SMPL-X predictions from the official retrained StreamTalk CFG checkpoints. This release follows the convenient inference-data style of [SemTalk](https://github.com/Xiangyue-Zhang/SemTalk): users can download generated test outputs and evaluate or visualize them without rerunning WavLM, retrieval, diffusion sampling, and SMPL-X conversion.
- [Paper](https://arxiv.org/abs/2608.01643) · [Hugging Face Paper page](https://huggingface.co/papers/2608.01643)
- [Project page](https://xiangyuezhang.com/StreamTalk/) · [Code](https://github.com/Xiangyue-Zhang/StreamTalk)
- [Model checkpoints](https://huggingface.co/X-Zhang/StreamTalk)
No BEAT2 ground-truth motion, audio, training cache, or model checkpoint is included in these archives.
## Downloads
| Scope | Clips | Checkpoint | CFG | Archive | SHA-256 |
| :-- | --: | :-- | --: | :-- | :-- |
| Speaker 2 (Scott) | 15 | epoch 946 | 3 | [download](https://huggingface.co/datasets/X-Zhang/StreamTalk-Inference-Data/resolve/main/streamtalk_speaker2_e0946_cfg3_npz.tar.gz) | `859cb968c04804a797e3f8eb17aeba1f91bda29305f970564c9be481e41fa452` |
| All speakers | 265 | epoch 940 | 3 | [download](https://huggingface.co/datasets/X-Zhang/StreamTalk-Inference-Data/resolve/main/streamtalk_all_e0940_cfg3_npz.tar.gz) | `295d933883dc40acb9df5f2d2ba02767ec62c9d83f423a78900932499fc8edb4` |
```bash
wget https://huggingface.co/datasets/X-Zhang/StreamTalk-Inference-Data/resolve/main/streamtalk_speaker2_e0946_cfg3_npz.tar.gz
wget https://huggingface.co/datasets/X-Zhang/StreamTalk-Inference-Data/resolve/main/streamtalk_all_e0940_cfg3_npz.tar.gz
sha256sum -c SHA256SUMS
tar -xzf streamtalk_speaker2_e0946_cfg3_npz.tar.gz
tar -xzf streamtalk_all_e0940_cfg3_npz.tar.gz
```
## Released FGD re-score
The downloadable NPZ bytes were independently re-scored with the StreamTalk release scorer and the pinned EMAGE/PantoMatrix AESK encoder (`SHA-256 5cd9566b...c40f55`). Equal-length metric batching uses batch size 16.
| Scope | Archive re-score FGD ↓ | Selection-time FGD ↓ | Absolute difference |
| :-- | --: | --: | --: |
| Speaker 2 (Scott) | **0.3788789702354345** | 0.3788789702354416 | 7.1e-15 |
| All speakers | **0.21764184426140076** | 0.21767150775461275 | 2.97e-5 |
Both differences are below the selected engineering reporting tolerance of `1e-3`. These are accelerated EMAGE/AESK measurements, not a claim of paper-exact B=1/M=1/full-window bitwise equivalence. See `speaker2_fgd.json` and `all_fgd.json` for the complete metric contract.
## NPZ schema
Every archive contains one `res_<BEAT2-test-id>.npz` per official test ID plus a scope manifest. Load files with `allow_pickle=False`.
| Key | Shape/type | Meaning |
| :-- | :-- | :-- |
| `poses` | `[T, 165]`, float32 | local SMPL-X axis-angle rotations |
| `trans` | `[T, 3]`, float32 | root translation |
| `expressions` | `[T, 100]`, float32 | facial expression coefficients |
| `betas` | `[300]`, float32 | body-shape coefficients |
| `gender` | string | body-model metadata |
| `mocap_frame_rate` | scalar | 30 FPS |
| `model` | string | `smplx2020` |
```python
import numpy as np
with np.load("res_2_scott_0_1_1.npz", allow_pickle=False) as motion:
poses = motion["poses"]
expressions = motion["expressions"]
trans = motion["trans"]
```
## Re-score with StreamTalk
The generated files do not include BEAT2 ground truth. After obtaining BEAT2 under its own terms, use the scorer in the [StreamTalk repository](https://github.com/Xiangyue-Zhang/StreamTalk):
```bash
python tools/evaluate_generated_fgd.py \
--predictions streamtalk_speaker2_e0946_cfg3_npz \
--ground_truth /path/to/beat_english_v2.0.0/smplxflame_30 \
--scope speaker2 --device cuda:0 --metric_batch_size 16
python tools/evaluate_generated_fgd.py \
--predictions streamtalk_all_e0940_cfg3_npz \
--ground_truth /path/to/beat_english_v2.0.0/smplxflame_30 \
--scope all --device cuda:0 --metric_batch_size 16
```
## Provenance and terms
- Generator code: StreamTalk training/evaluation commit `b6cac99d5bae5164a21f5db25afef72c3c30556a`.
- Speaker 2 checkpoint SHA-256: `07065d52e333cc48960e4e8fad5c0331f6b9e482f14aa1b78cb721904da5b075`.
- All-speaker checkpoint SHA-256: `3a0e3558d788fd3f11a5a87a8198b42725104b5b9b24f95a4e7c84c14395c9a5`.
- Seed: 0. Speaker 2 generation uses clip batch 1; all-speaker generation uses clip batch 16; both use CFG 3 and an 8-frame retrieval query.
The predictions are derived from experiments on BEAT2 and use the SMPL-X body model. Users remain responsible for complying with the licenses and terms of the [BEAT2 dataset](https://huggingface.co/datasets/H-Liu1997/BEAT2) and [SMPL-X](https://smpl-x.is.tue.mpg.de/). This dataset card does not redistribute BEAT2 ground truth, audio, or SMPL-X model files.
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