A generated dance clip can look fine as a thumbnail and still fail in motion: the face drifts, feet slide, the torso stretches, or the apparent hip movement comes from the camera rather than the body.
This tutorial turns that subjective review into a small OpenCV test. The input fixture and five-second result come from the public example on the Voor AI Hip Shake generator; no generation credits are needed to reproduce the analysis.
Define the signals
For each sampled frame, track four normalized values:
- face-center drift from the baseline;
- torso-width ratio;
- left and right foot displacement;
- horizontal hip-center amplitude.
Normalize pixel distances by frame width or subject height. That makes thresholds portable across exports.
from dataclasses import dataclass
@dataclass
class FrameMetric:
t: float
face_drift: float
torso_ratio: float
left_foot_drift: float
right_foot_drift: float
hip_x: float
def failed(m: FrameMetric) -> list[str]:
errors = []
if m.face_drift > 0.035:
errors.append("face drift")
if not 0.90 <= m.torso_ratio <= 1.10:
errors.append("torso stretch")
if max(m.left_foot_drift, m.right_foot_drift) > 0.04:
errors.append("foot slide")
return errors
The values above are starter thresholds, not universal biological rules. Calibrate them against a small set of clips your team has already accepted.
Sample deterministically
OpenCV makes a reproducible one-frame-per-half-second pass straightforward:
import cv2
cap = cv2.VideoCapture("hip-result.mp4")
fps = cap.get(cv2.CAP_PROP_FPS)
step = max(1, round(fps * 0.5))
frames = []
i = 0
while True:
ok, frame = cap.read()
if not ok:
break
if i % step == 0:
frames.append((i / fps, frame))
i += 1
assert frames, "video produced no decodable frames"
Keep the raw timestamp with every metric. “The face drifts at 3.5 seconds” is actionable; “the face looks weird” is not.
Separate intended motion from instability
Hip amplitude should oscillate. Face identity and torso proportions should not. A simple release rule can require:
def release_ok(metrics):
drift_failures = sum(bool(failed(m)) for m in metrics)
hip_span = max(m.hip_x for m in metrics) - min(m.hip_x for m in metrics)
return drift_failures == 0 and hip_span >= 0.06
This catches two opposite failures: unstable anatomy and a “dance” clip with almost no measurable lateral motion.
Preserve the generator state with the report
The current page shows a required image input, a 5-second default within a documented 4–30 second range, Public watermarked visibility, and 628 credits for the selected state. Save these observations with the test artifact because a model or setting change can invalidate old baselines.
Do not claim that a UI success toast equals a passing video. Decode the final MP4, count frames, and run the metric gate.
Turn failures into a compact CI artifact
Emit JSON plus a contact sheet of the worst timestamps. A pull request reviewer should be able to see the evidence without replaying the whole clip.
{
"duration_s": 5.04,
"sample_interval_s": 0.5,
"release_ok": false,
"failures": [{"t": 3.5, "type": "face drift", "value": 0.041}]
}
Use the public fixture page and its real result as a reproducible starting point. The aim is not to automate taste; it is to make identity, anatomy, and motion regressions visible before a clip ships.
