TL;DR: A creator's workflow chains Claude (as director/prompt writer), GPT Image 2 (character identity sheet), and Seedance 2.0 via Higgsfield (beat animation) to produce 15-second character-consistent UGC-style clips in about 45 minutes instead of days. The core trick: build one multi-angle reference sheet up front instead of fighting identity drift with lock-strength settings.

The actual problem this solves

Anyone who's messed with AI video generation knows the tell: the character's face, hair, or outfit subtly shifts between cuts. It's the single fastest way to make a clip read as "obviously AI." Most people try to fix this by cranking a reference-strength slider on one still image and hoping it holds for the whole clip. It usually doesn't, especially across multiple shots.

This workflow instead builds a character identity sheet first — one image containing 5 body angles, 7 facial expressions mapped to story beats, plus close-up insets for hair, fabric, and accessories. Every beat of the final video renders against that same sheet instead of a single lucky still.

![Pipeline diagram: Claude directs GPT Image 2 and Seedance 2.0 through beat generation to a finished clip](https://mer.vin/wp-content/uploads/2026/07/claude-cinematic-ugc-pipeline.png)

How the pipeline works

Three steps, three tools:

1. Claude writes the identity sheet spec — angles, expressions, fabric/accessory close-ups — and hands it to GPT Image 2 to render as a single asset.
2. Claude breaks the 15s clip into 3–5 beats, each one naming a specific expression + angle from the sheet, a continuity anchor to the next beat (same hoodie zip position, same lighting direction, etc.), and a timing budget.
3. Seedance 2.0 (via Higgsfield) animates each beat with the identity sheet attached, reportedly at ~0.97 character strength. No rotoscoping, no per-shot guesswork — every render pulls from the same reference.

Sample beat template from the source:

- Beat 1 (0–3s): establishing shot, front angle, neutral expression, handheld POV
- Beat 2 (3–7s): reaction, three-quarter angle, smile expression, same hoodie zip as beat 1
- Beat 3 (7–12s): payoff, close-up inset, identity sheet attached, character_strength 0.97

A CapCut pass is often used afterward to scrub residual AI artifacts while keeping the "shot on a phone" look intact.

Claimed numbers (take with appropriate salt)

- 15s cinematic clip in ~45 minutes vs. ~3 days traditional production
- ~10x more slice-of-life variants testable per launch before spending on paid media
- Continuity driven by sheet architecture, not trial-and-error lock strength

Worth flagging: these figures come from one creator's public workflow post, not an independent benchmark or vendor spec sheet. No independent verification here — results will depend heavily on prompt quality, the specific Seedance settings, and how much manual cleanup you're willing to do.

Where it works, where it doesn't

Good fit:

- Slice-of-life / BTS vlog style (handheld, natural light, minimal grading)
- Short vertical formats where consistency across cuts is highly visible
- Rapid creative testing before committing to paid media
- DTC ads needing "real person" footage without a UGC creator budget

Not a great fit:

- Anything involving regulated or celebrity likeness — legal review required
- Any use case where you need the actual prompt templates, since the creator's original post gates the full templates behind a comment CTA ("SLICE")
- Anyone expecting zero manual polish — a cleanup pass is still commonly needed

My take

The "identity sheet first, lock strength second" framing is the actually useful idea here, independent of whether the specific 45-minute number holds up for you. If you're already doing AI UGC and fighting drift, building a proper multi-angle reference sheet before you touch the video model is a cheap thing to test regardless of which video model you're using.

Curious if anyone here has tried a similar sheet-first approach with other video models (Kling, Runway, Pika, etc.) — does the same structural fix carry over, or is this specific to how Seedance handles reference attachment?

Media source (Article): https://mer.vin/2026/07/claude-gpt-image-2-and-seedance-2-0-cinematic-ai-video-workflow-for-character-locked-ugc/

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Claude GPT Image 2 and Seedance 2.0: Cinematic AI Video Workflow for Character-Locked UGC