Reverse Engineer Animation

• IS: measuring motion that already exists in a recording (extract frames, track per frame, fit curves, annotate choreography), then emitting code and a handoff spec that reproduce it.
• IS NOT: designing new motion from scratch, or reviewing/debugging motion code. Route both to ui-animation.

Contents

• Reference files
• Dependencies
• Workflow (extract, vision pass, track, fit, annotate, emit, validate)
• Gotchas
• Validation
• Related skills

Reference files

Dependencies

• ffmpeg: frame extraction (brew install ffmpeg). Required for step 1.
• Python: pip install opencv-python numpy scipy for tracking + curve fitting (step 4).

Extraction degrades gracefully: with only ffmpeg you can still extract frames and reason visually. Tracking and fitting need the Python packages.

Workflow

Copy and track this checklist:

Reverse-engineer progress:
- [ ] Step 1: Extract frames + contact sheet (per direction if open differs from close)
- [ ] Step 2: Vision pass: identify element, effects, phases
- [ ] Step 3: Decide precision (eye-only vs scripted)
- [ ] Step 4: Track motion and fit curves (if escalating)
- [ ] Step 5: Annotate choreography (delays, asymmetry)
- [ ] Step 6: Emit code for the target(s)
- [ ] Step 7: Validate against the recording

Step 1: Extract frames

Run python3 scripts/extract_frames.py <video> <outdir>. On a multi-second recording, trim to just the transition with --start SECONDS --duration SECONDS; extracting the whole clip floods the contact sheet and dilutes tracking. If the interaction has both an open and a close, trim two windows and run the whole pipeline once per direction. They are almost never mirror images, so one measurement cannot serve both.

Match --fps to the source: probe with ffprobe -v 0 -select_streams v -show_entries stream=avg_frame_rate <video> and never sample above the source rate (see Gotchas). Open the generated contact_sheet.png first; it shows the whole timeline at once.

Step 2: Vision pass

View the contact sheet and name, in order:

• The element(s) that move.
• Every effect present: translate, scale (often anisotropic), opacity, blur, corner radius, shadow, color. Use the property checklist in references/measurement-guide.md.
• The phases (e.g. backdrop blurs in, element tucks under the notch, over-stretches, then settles per-edge). Note which property leads and which lags.

Step 3: Decide precision

• Simple fade or linear slide: read timing off the contact sheet and skip to step 5.
• Elastic, springy, or multi-property motion: escalate to step 4. Eyeballing a spring is unreliable.

Step 4: Track and fit

Run python3 scripts/track_motion.py <outdir> to produce metrics.json. Pass --bbox X,Y,W,H to restrict detection to one element when several move (one run per element). Then run python3 scripts/fit_curves.py <outdir>/metrics.json to get spring params, cubic-bezier, and a fit error per property. If you extracted with a non-default --fps, pass the same --fps to fit_curves.py: its default is 30 and a mismatch rescales every duration and stiffness (see Gotchas). Read references/curve-fitting.md to interpret the numbers and pick the model. High error on both models means multi-phase motion: split the timeline and fit each segment.

Step 5: Annotate choreography

Load references/choreography.md. Build the timing-offset table (when each property starts and settles). Those lead/lag gaps and the over-stretch carry more of the feel than any single curve.

Step 6: Emit code

Substitute the fitted parameters into the templates in references/code-output.md for the requested target. Keep movement on transform/opacity. Emit two transitions when open and close differ. Produce the consolidated handoff motion spec (timing table + curves + snippet) from references/code-output.md so the result can be implemented without the video.

Step 7: Validate

Run the Validation checks below. The exit criterion is comparison evidence, never "looks right".

Gotchas

• fit_curves.py defaults to --fps 30. Extract at --fps 60 and fit at the default, and every duration_ms doubles (a 500 ms transition reports as 1000 ms) while fitted stiffness drops to a quarter. Always pass the extraction fps to the fit.
• Sampling above the source frame rate duplicates frames: a 24 fps GIF extracted at --fps 60 yields runs of identical values in metrics.json that plateau the progress curve and inflate both fit errors. Probe the source rate first and match it.
• Screen recordings drop frames under load, and iOS/QuickTime captures are variable-frame-rate. Consecutive identical metrics.json rows are duplicated frames, not a pause in the animation. If plateaus dominate, re-record at a steadier frame rate instead of chasing the fit.
• Fitting one global curve to multi-phase motion fails with high error on both models. Split into phases and compose keyframes (references/curve-fitting.md).
• Tracking only position misses blur/opacity, which usually lead the move by ~100 ms. That lead is where the polish lives.
• Open and close are never mirror images (open slower and springier, close faster and flatter). Measure each direction as its own clip; never reuse the open curve reversed.
• Reporting spring params without checking the fit error: a bad fit gives confident-looking but wrong numbers; treat error > 0.08 as suspect.
• Emitting code that animates layout props (width, height, top, left): reproduce the motion on transform/opacity/filter (defer to ui-animation rules).
• Re-reading a script to reconstruct its logic instead of running it: the scripts are the canonical, deterministic path.

Validation

• Re-derive: play the emitted animation, screen-record it, run it back through extract_frames.py, and compare contact sheets side-by-side with the original.
• Slow to 0.1x in DevTools to confirm phase order (lead/lag) and the over-stretch survive.
• Confirm the emitted code only animates transform, opacity, and filter.
• Sanity-check fitted spring overshoot/zeta against what you saw; a clear bounce must not fit as a flat ease.

Related skills

• ui-animation: turn the extracted spec into production-grade, interruptible motion and apply its easing defaults and anti-pattern rules.