Skip to content
cccskills
FreePublished on GitHub

product-photo-to-video

Create product videos for ads and listings from one to three product photos while preserving the real packaging and label. Build a storyboard, generate a multi-shot clip, repair shots where the product drifts, and composite the real packshot into the ending.

OriginalShow the original description

Turn a product photo into a product video ad that keeps the real product. Builds TikTok, Reels, TikTok Shop b-roll and listing videos from one to three product photos: a product truth sheet (label text letter by letter, colours, parts), a cut-out, a hero still, an 8–12 panel storyboard sheet, then one multi-shot Seedance 2.5 or Kling 4 clip with a PRODUCT LOCK block, a repair pass on drifting shots, and the real packshot composited on the last second. Formats: hands-only ASMR unboxing, macro pour or texture, ingredient burst or exploded view, product in use, 360° loop, product in a hand, apparel try-on. Never ships a Ken Burns slideshow. Use when someone has product photos and wants a product video, packshot animation, unboxing, ASMR product video, listing video or AI b-roll without filming. NOT for a person talking about the product (use ai-ugc-ad-video), TikTok Shop presenter videos (use tiktok-shop-video), or still listing images (use a product-photo skill).

hashgraph-online/awesome-codex-plugins1,440 stars(GitHub 1,440 + cccskills 0)
Works withTikTok

Run score Not tested

See how to install

Good for

  • Making a TikTok or Reels product ad from product photos
  • Creating an ASMR unboxing or close-up product demo without filming
  • Generating product listing videos or AI b-roll
  • Animating a packshot while keeping its real label and shape

Files included (3)

  • SKILL.md8.5 KB
  • references/failures.md2.4 KB
  • references/prompts.md6.7 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Turn a product photo into a product video ad

What wins in late 2026 is one impossible but tactile moment built around the real product: a macro pour, a lid snap, an ingredient burst, a hands-only unboxing. What fails: logo-first "cinematic" b-roll, still-photo slideshows (TikTok Shop bans them in shoppable videos), and any frame where the model rewrote the label.

Generation runs on varg (MCP call_tool or POST https://api.varg.ai/v2/tools/{image|video}/call). Quote with estimate_cost and wait for a yes before each paid step. A still costs cents to fix; a clip costs dollars, so approve stills first.

1. Product truth sheet (free, do it first)

From the photos and the product page, write:

PRODUCT: <name, size, e.g. "30 ml amber glass dropper bottle, gold cap">
LABEL (exact, in order): "NORTHLINE" / "VITAMIN C" / "30 ML"
COLOURS: body #F4A23B, cap #C9A227, label #FFFFFF
PARTS / IN THE BOX: bottle, dropper, box
SCALE: fits in the palm
CLAIMS WE CAN SHOW: absorbs in ~30 s (visible), lightweight texture
NEVER: change shape, size, colour or material; add text; fake results (fizz, instant clearing)

Photos: clean, straight-on, label readable. "If the label isn't readable in the reference, the model invents its own text."

2. Cut-out and hero still

  1. Cut-out: bria_guided_remove_background (keeps the original pixels).
  2. Hero still: gpt_image_2_5 edit, "keep the uploaded product exactly unchanged, replace only the background with [environment], match reflections and contact shadows, no product deformation, no added labels". nano_banana_2_1 is cheaper but may need small packaging text fixed.
  3. 9:16 canvas: bria_fibo_edit_1_5_expand. Real side views for reveals: qwen_angles (exact angles, not invented sides).
  4. Check at 100%, letter by letter, colour by eyedropper. Reject and redo, don't "fix later".

Product in a hand: bria_fibo_edit_1_5_product_holding. Apparel on a person: bria_fibo_edit_1_5_virtual_try_on (check stitching, print and logo; AI try-on smooths texture and length).

3. Storyboard sheet (approve before video)

gpt_image_2_5, 8–12 panels, each ≥1.5 s with one camera move, POV hands only for no-face formats. Template: references/prompts.md P1. Sequence that works: product on the surface, hands enter → open and reveal → the most tactile moments → in use → the result in macro → hero packshot. Show it to the person.

4. One multi-shot clip with PRODUCT LOCK

seedance_2_5, 10–15 s, @image1 = the cut-out, @image2 = the approved sheet ("follow its shot order, never show its panels, numbers or text"), audio on for foley, "no background music" unless wanted, last second held clean for the end card. Template: references/prompts.md P2.

Cheaper: kling_v4 multi-shot (often right first time) or wan_3 (best value for b-roll volume). Drafts: kling_v4_flash.

Safest for listings and loops: pixelcut_looping_video (locked camera, seamless loop, motion: spin for 360°). Exact orbit around a packshot: minimax_h3_max_camera_controls.

5. Repair, don't reroll

Reject any shot where the pack, label, shape, colour, part count or hand count drifts. Regenerate only that shot from first and last frames (seedance_2_5 first/last-frame mode, kling_v4 end_image_url), camera locked in both frames or it invents a move. Reliability ladder, best first:

  1. Composite the real product PNG and label over AI footage (real pixels)
  2. Locked camera, only the environment moves (liquid, fog, hands)
  3. Reference on every shot + PRODUCT LOCK + label spelled out
  4. One job per reference ("defines the product ONLY")
  5. First/last frames from approved stills
  6. A hand anchored on the box base; hands at the product's edges
  7. Push-ins and parallax instead of full spins of unseen sides

Common failures and fixes: references/failures.md.

6. Composite the truth and export

  • Lay the real packshot PNG (and copy, if any) over the clean last second:

    # packshot.png = the real cut-out with alpha; 13 = where the clean last seconds start
    ffmpeg -i clip.mp4 -loop 1 -i packshot.png -filter_complex "[1:v]scale=760:-1[p];[0:v][p]overlay=(W-w)/2:(H-h)/2:shortest=1:enable='gte(t,13)'" -c:v libx264 -crf 18 -c:a copy hero.mp4
    

    Tested on FFmpeg 6.0: the packshot appears from 13 s and the clip keeps its length.

  • Edit, captions, CTA, loudness and the 4:5 cut: ecommerce-video-edit. A 1:1 or 16:9 15–30 s cut with no hard sell for the product page.

  • Label realistic AI footage (TikTok AIGC toggle; Amazon contains-synthetic-performer if a photoreal person appears). Never show the product doing more than it can.

Formats

FormatModelNotes
Hands-only ASMR unboxingseedance_2_5 / kling_v412-panel ASMR sheet; "natural ASMR only"; one hand stays on the box base
Macro pour / texture / splashseedance_2_5Locked camera on the pack; only the liquid moves
Ingredient burst / exploded viewgpt_image_2_5 still → seedance_2_5Real parts in their real order
Food process film ending on the productseedance_2_58–12 beats of 2–3 s, match cuts, 110–120 BPM
360° loop for listingspixelcut_looping_videoSafest label fidelity
Product in use (kitchen, bathroom)nano_banana_2_1 stills → kling_v4Hands at edges, real scale cue
Apparel on a modelbria_fibo_edit_1_5_virtual_try_on → seedance_2_5One @image per garment, "EXCLUSIVE reference"

Costs on varg (live rules, Oct 2026; quote exact jobs with estimate_cost): a 15 s product video is roughly 250–1,000 credits at 720p (Seedance 2.5 is the priciest and varies ~2× by route; Kling 4 ~16 credits/s, Wan 3 ~10 credits/s at 720p).

Related

ecommerce-video-ads · varg-product-hook (UGC-style hook clip of the product in use) · ecommerce-video-edit · ai-ugc-ad-video · tiktok-shop-video · varg-ai

Sources: varg how-to: turn a product photo into a video, fal commercial skill, Masonry controlled test, Luma Seedance guide, OpenAI image prompting cookbook, X posts by @abxxai, @Ciri_ai, @HeyRu0by, @Strength04_X, @yourPlugAI.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Plan worthwhile local tasks by checking dependencies, effects, resources, and platform boundaries, then execute the exact approved plan. It can use safe parallel work or suitable Apple-silicon backends when the project exposes them.

  • Speed up independent local build or test steps
  • Plan a task with several dependency-linked operations
  • Choose an Apple-silicon backend for supported numerical or media work
hashgraph-online/awesome-codex-plugins1.4KUpdated Oct 27, 2026

Turn a winning TikTok or Meta ad into a structured creative test plan. Identify what made it work, plan distinct concepts, set test budgets and review timing, and use funnel metrics to decide whether to stop, iterate, or scale each ad.

  • Planning how many ad concepts to test and what budget to assign
  • Creating ad variants that differ in buyer, angle, format, or opening frame
  • Diagnosing why variants perform alike or where an ad loses viewers
hashgraph-online/awesome-codex-plugins1.4KUpdated Oct 30, 2026

add

FreeSummary

Record deferred engineering decisions and temporary workarounds in a debt registry, with context such as effort, ongoing cost, affected area, and payoff trigger. Entries receive short labels so they can be reviewed or dropped later.

  • Logging a TODO or unfinished implementation
  • Recording a temporary default or bypassed check
  • Tracking a swallowed error or loosened type
hashgraph-online/awesome-codex-plugins1.4KUpdated Oct 30, 2026

Generate or compare AI agent code for LangChain, EINO, AutoGen, AgentScope, and CrewAI using framework-specific templates, API mappings, conventions, and project structures. Reference files include verification dates to help identify potentially outdated guidance.

  • Creating an agent project for a chosen framework
  • Comparing APIs across supported agent frameworks
  • Referencing framework-specific tool and memory patterns
hashgraph-online/awesome-codex-plugins1.4KUpdated Oct 26, 2026

Investigate a reproducible bug, test failure, or unexpected page behavior, then make a narrowly scoped fix. The workflow calls for a regression test before changing code, verifies the fix, and reports remaining risks and evidence while avoiding unrelated refactoring or feature work.

  • Finding the cause of a failing test
  • Fixing an unexpected page or feature behavior
  • Adding a regression test before a minimal code change
hashgraph-online/awesome-codex-plugins1.4KUpdated Oct 30, 2026

Build an AI agent project through six collaborative stages, from requirements and architecture to tools, prompts, code generation, and verification. Start framework-independent, then generate a runnable project for frameworks such as LangChain, EINO, AutoGen, AgentScope, or CrewAI.

  • Designing an AI agent from a business need
  • Generating a runnable agent project
  • Planning tools, prompts, and orchestration
hashgraph-online/awesome-codex-plugins1.4KUpdated Oct 26, 2026

See all skills by hashgraph-online

Report a problem with this skill