Polished talking-head YouTube edit with explanatory motion graphics
Polished talking-head YouTube edit with explanatory motion graphics is a 378.8-second Claude Opus motion design reference by @condzxyz, built around Remotion, Python. [Note: the creator shared a partial prompt. Copy this motion graphics prompt, keep the creator's frame counts and palette, and adapt the subject for your own launch film.
@condzxyzView post
Prompt
[Note: the creator shared a partial prompt. Wording below is theirs.]
GOAL:
Edit my talking-head video into a polished YouTube video with explanatory motion graphics.
MOTION:
INPUT
- Raw video: . Speech is Portuguese; don't translate.
- Target: at most . Style: premium, clean, editorial ("Anthropic/Apple level").
- End card: subscribe + follow me on X () + "comment what you want to see next".
- Never cover my face. Never show where I live.
STACK
- Remotion (https://t.co/RONMuXavg6) for the composition, ffmpeg for cuts and encoding, Python for analysis.
- Transcribe with word timestamps using ElevenLabs Scribe. It has precise word boundaries and marks fillers like "hã"/"é".
- If you use Whisper instead: its word ends stretch over pauses, so refine every cut point with the audio energy.
1) PLAN THE CUT
- Make an indexed transcript (word index → text, start, end).
- Choose keep-ranges by word index. Remove fillers, false starts, repeated sentences and weak tangents until it fits the target.
- Put the strongest line (2–4 s) at the very start as a cold-open hook.
2) CUT, FRAME-ACCURATE AND IN SYNC
- Cut points:
- Cut in the silence between words, never inside a word.
- Keep ~50 ms before the first word and ~60 ms after the last word (~160 ms if it ends a sentence).
- Silences inside kept ranges:
- Detect them with a 10 ms RMS envelope. A threshold around −38 dBFS worked here; measure your own noise floor first.
- Shorten any pause over 0.32 s to 0.22 s (0.36 s after a sentence ends).
- Round every boundary to whole frames (30 fps) and merge touching segments.
- Video: ffmpeg split → trim(start_frame, end_frame) → concat, plus a light grade (hqdn3d, eq, colorbalance, unsharp) → intermediate at CRF 14.
- Audio: cut the same boundaries in numpy with 8 ms fades and no overlap. A/V stays in sync.
- QA: re-transcribe the cut and diff it against the intended text. Fix leaked fragments and clipped word starts, then repeat until clean.
3) FACE TRACK
- Detect the face every 0.2 s (Apple Vision or MediaPipe).
- Smooth with a moving average inside each cut segment, never across a jump cut.
- Use it to frame the camera and to place graphics on the side away from the face.
4) DESIGN SYSTEM
- One palette: dark warm stage #14120F, ivory "paper" #F1ECE2, one accent (clay #D97A4E).
- Fonts: Fraunces (serif display), Inter Tight (UI), JetBrains Mono (labels). Add subtle grain and soft glows.
- No gradient text, no neon.
- Camera layout modes, morphing between them in ~0.7 s (cubic in-out):
- full frame;
- split left/right: rounded camera card 856×936, face at 42% of its height, graphics on the other half;
- PIP: 256 px circle over a full-screen paper sheet, for chapter moments.
5) BEAT SHEET (JSON)
- Give every sentence group a visual, cued to word start times in the cut timeline:
- lower third (name/role);
- big numbers with count-up;
- lists that reveal on each spoken item;
- SVG diagrams
Author original
Edit my talking-head video into a polished YouTube video with explanatory motion graphics.
INPUT
- Raw video: . Speech is Portuguese; don't translate.
- Target: at most . Style: premium, clean, editorial ("Anthropic/Apple level").
- End card: subscribe + follow me on X () + "comment what you want to see next".
- Never cover my face. Never show where I live.
STACK
- Remotion (https://t.co/RONMuXavg6) for the composition, ffmpeg for cuts and encoding, Python for analysis.
- Transcribe with word timestamps using ElevenLabs Scribe. It has precise word boundaries and marks fillers like "hã"/"é".
- If you use Whisper instead: its word ends stretch over pauses, so refine every cut point with the audio energy.
1) PLAN THE CUT
- Make an indexed transcript (word index → text, start, end).
- Choose keep-ranges by word index. Remove fillers, false starts, repeated sentences and weak tangents until it fits the target.
- Put the strongest line (2–4 s) at the very start as a cold-open hook.
2) CUT, FRAME-ACCURATE AND IN SYNC
- Cut points:
- Cut in the silence between words, never inside a word.
- Keep ~50 ms before the first word and ~60 ms after the last word (~160 ms if it ends a sentence).
- Silences inside kept ranges:
- Detect them with a 10 ms RMS envelope. A threshold around −38 dBFS worked here; measure your own noise floor first.
- Shorten any pause over 0.32 s to 0.22 s (0.36 s after a sentence ends).
- Round every boundary to whole frames (30 fps) and merge touching segments.
- Video: ffmpeg split → trim(start_frame, end_frame) → concat, plus a light grade (hqdn3d, eq, colorbalance, unsharp) → intermediate at CRF 14.
- Audio: cut the same boundaries in numpy with 8 ms fades and no overlap. A/V stays in sync.
- QA: re-transcribe the cut and diff it against the intended text. Fix leaked fragments and clipped word starts, then repeat until clean.
3) FACE TRACK
- Detect the face every 0.2 s (Apple Vision or MediaPipe).
- Smooth with a moving average inside each cut segment, never across a jump cut.
- Use it to frame the camera and to place graphics on the side away from the face.
4) DESIGN SYSTEM
- One palette: dark warm stage #14120F, ivory "paper" #F1ECE2, one accent (clay #D97A4E).
- Fonts: Fraunces (serif display), Inter Tight (UI), JetBrains Mono (labels). Add subtle grain and soft glows.
- No gradient text, no neon.
- Camera layout modes, morphing between them in ~0.7 s (cubic in-out):
- full frame;
- split left/right: rounded camera card 856×936, face at 42% of its height, graphics on the other half;
- PIP: 256 px circle over a full-screen paper sheet, for chapter moments.
5) BEAT SHEET (JSON)
- Give every sentence group a visual, cued to word start times in the cut timeline:
- lower third (name/role);
- big numbers with count-up;
- lists that reveal on each spoken item;
- SVG diagrams
Questions
What is this motion graphics prompt for?
It is a Claude Opus motion design brief for “Polished talking-head YouTube edit with explanatory motion graphics”. Paste it into Claude and keep the timings already written in the prompt.
Where is the original film?
The creator is @condzxyz. Use View post to open the X video this prompt belongs to.