Download README.md from DaveRc/ghosttrial-g1-scorpion-motion: direct link, hf CLI and curl.
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https://huggingface.co/datasets/DaveRc/ghosttrial-g1-scorpion-motion/resolve/1f91fbe8d2c86c8e6e386e79e5a40ccc57d1dfd9/README.md
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curl -L -o README.md https://huggingface.co/datasets/DaveRc/ghosttrial-g1-scorpion-motion/resolve/1f91fbe8d2c86c8e6e386e79e5a40ccc57d1dfd9/README.md
license: cc-by-4.0
task_categories:
- robotics
tags:
- motion-capture
- humanoid
- unitree-g1
- retargeting
- motion-tracking
pretty_name: GhostTrial — spear throw into an uppercut, retargeted to a Unitree G1
size_categories:
- n<1K
GhostTrial motion data — a spear throw into an uppercut, on a Unitree G1
The motion behind DaveRc/ghosttrial-g1-scorpion: a two-part combat phrase — spear throw, pull back, crouch into a rising uppercut — captured from a human performer and carried all the way to the G1's 29 degrees of freedom.
Pipeline, tools and full write-up: https://github.com/SpiRaiL/GhostTrial-public
Where it came from
A movement phrase written as a brief and filmed by a commissioned performer
(docs/upwork_capture_brief.md in the GitHub repo), across several camera angles
and two shoots. Monocular video → 3D human pose → human skeleton in BVH →
retargeted to the G1 → corrected for self-collision, foot contact and joint limits
→ converted to the motion_lib format SONIC trains on.
The performer's video is not published, only the motion derived from it. The 1992 Mortal Kombat footage that inspired the move is style reference only; it never entered training and is not distributed here.
What is in it
| folder | what it is |
|---|---|
data/motion_lib_capture/robot/ |
the pickles SONIC trains on, one folder per take. t12/T12_beats.pkl is the one that trained the published policy |
data/gemx_g1_retimed/ |
the G1 takes as CSV, 29 DoF plus root — retimed, the working format |
data/gemx_g1_raw/ |
the same takes before retiming |
data/human_bvh/, data/human_bvh_edited/ |
the performer's own skeleton, as solved and as hand-corrected |
data/gemx_output/ |
per-take pose solve and the retarget straight out of it |
data/csv_frozen/ |
frozen reference poses: standing, idle, a walking control, and the crouch seeds |
data/capture_analysis/, data/capture02_analysis/ |
2D keypoints and per-take rankings from reviewing both shoots |
data/g1_authored/ |
one phrase authored by hand in G1 joint space, the legal fallback and the first thing that trained |
How the takes are named
Order of work, not quality:
- A2–A11 — retarget revisions. A9 is the reference the diagnosis work is built on; A10 is the take whose feet left the floor; A11 is faster and lower.
- S1, B1, CTRL, CTRL2 — standing, idle and walking controls, used to check the policy has not forgotten how to stand or walk.
- T1–T12 — target rebuilds after the crouch turned out to be the problem. T12 is the final one: the phrase with a 0.5 s lean and a 0.5 s crouch hold feathered in.
Known limits
- One performer, one phrase. This is a hackathon dataset, not a corpus.
- The target still asks for airborne frames the G1 cannot hold — the trained policy plants instead (97% double support against the target's 58%).
- Solved from monocular video, so depth is inferred rather than measured. The angle-45 takes are the most reliable.
Licence
CC BY 4.0. Derived from footage commissioned for this project, published with the performer's work credited as the source of the motion.