Boostify MotionDNA v0
MotionDNA is Boostify's human-motion model: a compact autoregressive Transformer that learns the DNA of how performers move in real music videos and can generate new, plausible motion sequences (2D skeletal pose) from a short seed.
This is the v0 checkpoint, trained end-to-end on motion extracted from real music-video clips. It is intentionally lightweight so it runs (and trains) on a single NVIDIA L4 (23 GB) — in fact training uses under 1 GB of VRAM.
What it does
- Input: a sequence of 2D human poses (17 COCO keypoints, x/y).
- Output: the predicted next pose, applied autoregressively to roll out new motion.
- Use cases: motion previews, choreography ideation, b-roll motion guides, music-video editing assistance inside Boostify Dataset Studio.
Architecture
- Causal Transformer encoder (next-pose prediction).
d_model=256,n_head=8,n_layer=6, windowWIN=64, input dim34(17×2).- ~4.77M parameters. Smooth-L1 loss. AdamW + cosine schedule.
Training data
Pose sequences extracted with YOLOv8-pose from real music-video clips (46 clips, 11,038 frames). Poses are hip-centered and torso-scaled, then standardized (mean/std stored inside the checkpoint).
Source footage is used internally for research/demo. The released artifact contains only the trained weights and normalization stats — no source video.
Files
| File | Description |
|---|---|
motiondna_v0_best.pt |
Best checkpoint (lowest train loss). |
motiondna_v0_final.pt |
Final-epoch checkpoint. |
motiondna_sample.npy |
Example generated motion [136, 17, 2]. |
loss_history.json |
Per-epoch training loss. |
extract_poses.py |
Pose-extraction pipeline (YOLOv8-pose → .npy). |
train_motiondna.py |
Training script. |
Inference
import torch, numpy as np
ckpt = torch.load("motiondna_v0_best.pt", map_location="cpu")
cfg = ckpt["cfg"]; mean = ckpt["mean"]; std = ckpt["std"]
# rebuild the MotionDNA module from train_motiondna.py, load_state_dict(ckpt["model"]),
# seed with N normalized poses and roll out model(x)[:, -1:] autoregressively,
# then de-normalize with: pose = gen * std + mean
Limitations
- v0 trained on a small clip set → motion is coherent but limited in style range.
- 2D pose only (no 3D, no global translation, no audio conditioning yet).
- Roadmap: more clips, music/beat conditioning, 3D lift, style control.
Citation
Boostify MotionDNA (2026). Trained with the Boostify Dataset Studio pipeline.