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Google Veo3 → I2V Prompting Guide. Playing with "human expression" Tips: + Guide the performance + Control facial expressions/gestures + It can handle finer details. + Don't overcomplicate the prompt BASE PROMPT STRUCTURE: [scene description], [expression/gesture description] (More exploration in thread 🧵) #google #veo3 #promptshare

Rory Flynn

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160,500 görüntüleme • 1 yıl önce •via X (Twitter)

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The Making of a Coliseum Ship Battle. With Google Earth + Weavy + Veo3 (With help from Runway/Flux/etc) PROCESS: 01. Screenshot Location on GE 02. Rexture + add details 03. Composite elements 04. Diagram Shot 05. Generate + iterate Used a lot of tools in this one. (More details in thread) #veo3 #promptshare

The Making of a Coliseum Ship Battle. With Google Earth + Weavy + Veo3 (With help from Runway/Flux/etc) PROCESS: 01. Screenshot Location on GE 02. Rexture + add details 03. Composite elements 04. Diagram Shot 05. Generate + iterate Used a lot of tools in this one. (More details in thread) #veo3 #promptshare

Rory Flynn

64,886 görüntüleme • 1 yıl önce

It's the small things with Veo3. Where you can see development in the tech. Object Permanence: + In this video...there's (2) "extends" + So (3) combined clips + The bushes stay consistent/proportional Small Details: + Road lines reflected on the car + Desert reflection on the car + Camera operator's reflection + The light shifting with the camera angle A few months ago these details wouldn't have made it. #google #veo3 #promptshare

It's the small things with Veo3. Where you can see development in the tech. Object Permanence: + In this video...there's (2) "extends" + So (3) combined clips + The bushes stay consistent/proportional Small Details: + Road lines reflected on the car + Desert reflection on the car + Camera operator's reflection + The light shifting with the camera angle A few months ago these details wouldn't have made it. #google #veo3 #promptshare

Rory Flynn

25,084 görüntüleme • 1 yıl önce

This Kling 3.0 UGC workflow is absolutely insane 🤯 It generates hyper-realistic UGC-style ad videos from a single text prompt — talking heads, unboxings, testimonials, tutorials — in one shot. Perfect for DTC brands and agencies who need to scale UGC without paying $500 per UGC video. Here's the problem: You're either paying creators who take weeks to deliver, or you're stitching together janky 5-second AI clips that look obviously fake. Kling 3.0 solves it: → Write a prompt with character, scene, and dialogue → Get a 10-15 second UGC video with realistic facial expressions → Native voice control — tone, pacing, emotion per line → Multi-shot scenes in one generation (hook → demo → CTA) → Paste negative rules to kill AI artifacts instantly No creator fees. No stitching clips. No uncanny valley. What you get: → 6 copy-paste prompt templates for every UGC format →Voice & dialogue cheat sheet with tone keywords → Multi-shot ad structure (hook → problem → solution → CTA) → Image-to-video workflow for branded product shots → A "where it fails" section so you don't waste credits → ChatGPT template to convert any rough idea into a Kling prompt I built a full prompting guide for UGC ads on TikTok and Meta. Want the guide? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

This Kling 3.0 UGC workflow is absolutely insane 🤯 It generates hyper-realistic UGC-style ad videos from a single text prompt — talking heads, unboxings, testimonials, tutorials — in one shot. Perfect for DTC brands and agencies who need to scale UGC without paying $500 per UGC video. Here's the problem: You're either paying creators who take weeks to deliver, or you're stitching together janky 5-second AI clips that look obviously fake. Kling 3.0 solves it: → Write a prompt with character, scene, and dialogue → Get a 10-15 second UGC video with realistic facial expressions → Native voice control — tone, pacing, emotion per line → Multi-shot scenes in one generation (hook → demo → CTA) → Paste negative rules to kill AI artifacts instantly No creator fees. No stitching clips. No uncanny valley. What you get: → 6 copy-paste prompt templates for every UGC format →Voice & dialogue cheat sheet with tone keywords → Multi-shot ad structure (hook → problem → solution → CTA) → Image-to-video workflow for branded product shots → A "where it fails" section so you don't waste credits → ChatGPT template to convert any rough idea into a Kling prompt I built a full prompting guide for UGC ads on TikTok and Meta. Want the guide? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Mike Futia

36,144 görüntüleme • 5 ay önce

You can now create AI images directly from Google Slides. No need to spend hours searching for images for your presentations. And this feature is available for free. Here's how to activate it: 1. Go to labs .google .com 2. Scroll down to "Google Workspace". 3. Click on the "Learn more" button to access the waitlist. When it's activated, you'll see the button that appears in Google Slides as in the video. Click on it and enter your prompt: E.g.: "a cat in front of a raspberry pie". You can even choose different styles: photography, vector art, sketch, ... This will save a lot of time when creating slideshows! Don't hesitate to follow me to learn how to do more with AI.

You can now create AI images directly from Google Slides. No need to spend hours searching for images for your presentations. And this feature is available for free. Here's how to activate it: 1. Go to labs .google .com 2. Scroll down to "Google Workspace". 3. Click on the "Learn more" button to access the waitlist. When it's activated, you'll see the button that appears in Google Slides as in the video. Click on it and enter your prompt: E.g.: "a cat in front of a raspberry pie". You can even choose different styles: photography, vector art, sketch, ... This will save a lot of time when creating slideshows! Don't hesitate to follow me to learn how to do more with AI.

Paul Couvert

318,257 görüntüleme • 2 yıl önce

currently experimenting with WAN 2.6 I2V on GMI Cloud in this test, I’m comparing two audio workflows and honestly both perform really well. one scene uses audio generated directly from the prompt, while the other uses manually uploaded audio taken from the film 300. visually, both deliver strong motion and solid performance. however, the version with audio coming straight from the prompt feels slightly more refined, camera movement is smoother, transitions flow more naturally and the sync between voice, facial motion, and pacing feels more cohesive. lip sync, especially for Chinese dialogue also comes across a bit cleaner. you can choose single shot for a clean, focused moment or multi shot if you want more cinematic transitions, even when working from just one reference image. one important note: always turn on prompt extension. it makes a noticeable difference in how well the model understands motion, transitions and overall scene flow. both audio approaches are totally usable, but if you’re building dialogue driven or cinematic scenes, starting with audio from the prompt gives WAN 2.6 a bit more context to work with. I’ll be pushing this further with more dynamic camera movement and transitions next. more experiments coming soon ✨ Wan

currently experimenting with WAN 2.6 I2V on GMI Cloud in this test, I’m comparing two audio workflows and honestly both perform really well. one scene uses audio generated directly from the prompt, while the other uses manually uploaded audio taken from the film 300. visually, both deliver strong motion and solid performance. however, the version with audio coming straight from the prompt feels slightly more refined, camera movement is smoother, transitions flow more naturally and the sync between voice, facial motion, and pacing feels more cohesive. lip sync, especially for Chinese dialogue also comes across a bit cleaner. you can choose single shot for a clean, focused moment or multi shot if you want more cinematic transitions, even when working from just one reference image. one important note: always turn on prompt extension. it makes a noticeable difference in how well the model understands motion, transitions and overall scene flow. both audio approaches are totally usable, but if you’re building dialogue driven or cinematic scenes, starting with audio from the prompt gives WAN 2.6 a bit more context to work with. I’ll be pushing this further with more dynamic camera movement and transitions next. more experiments coming soon ✨ Wan

DStudioproject

96,950 görüntüleme • 7 ay önce

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

📖THE STEP MOST CREATORS SKIP IS WHY THEIR AI ANIMATION LOOKS INCONSISTENT Consistency across clips doesn't come from prompting — it comes from the reference image. The pipeline, step by step: ▪ Start with ChatGPT Image 2 — generate a full character design sheet first, not just a single frame. Multiple angles, expressions, and outfit variations in one image keeps the character consistent across every scene ▪ Build a storyboard inside ChatGPT Image 2 as well — define each shot, camera angle, action, and mood before touching Seedance at all. This is the step most people skip and it's the reason clips look disconnected ▪ Define a color palette and lighting mood early — golden afternoon light, soft warm tones, dramatic shadows. Lock those values and repeat them across every prompt ▪ Take each storyboard frame into Seedance 2.0 as the reference image — one frame becomes one clip ▪ Write the Seedance prompt around the character action, not the scene description. The scene is already in the image. The prompt handles motion, camera behavior, and timing ▪ Keep clip duration between 4-6 seconds per shot — shorter clips give more control over pacing and reduce motion drift on character faces ▪ Match camera movement type across consecutive clips — if one shot dollies in, the next should hold or pull back, not dolly again The consistency across these frames comes from the character design sheet, not from luck. Seedance reads the reference image and the prompt together — if the reference is detailed enough, the output stays on-model. This video was created by ALOKXMEHTA 📥 tomorrow: the exact ChatGPT Image 2 prompt structure used to generate a multi-angle character design sheet like this one 🔖One article covers the entire workflow — it is pinned below, do not scroll past it.

Zentrix⌚️

12,846 görüntüleme • 1 ay önce

We have FINALLY solved how to generate infinite static ads completely on autopilot (including copy) and put them in a Drive folder, with ZERO human intervention necessary: 1. Generate ChatGPT headlines and push them into Google Sheets with Zapier. 2. Create one or several design templates for your brand in using their Canva integration 3. Set up a Zap that pushes your Google Sheet copy into the Switchboard template. It will automatically handle text formatting and centering 4. Auto upload the completed design file into Google Drive with Zapier You can do this with infinite design templates and different sources of copy (website reviews, subreddits, etc) so you ALWAYS have brand new ads READY TO GO, organized in the Drive folder of your choice for someone on your team to review and launch. The copy refreshes weekly in this zap according to your prompt. Comment if you want the SOP document for this that outlines it step-by-step!

Peter Czepiga

26,082 görüntüleme • 1 yıl önce

The body movements of Anime characters in Seedance doesn’t has to be always rigid and constrained. They can be as expressive as the real anime too. Here’s a quick tips on how to achieve the cartoony acting style for your anime characters: Special shoutout to Ameilo for sharing the prompt that unlocked this acting style. This tips sharing is made possible by Try ArtCraft Add this Key Prompt above your prompt: Emphasize snappy animation with lively energy, pose-to-pose, strong anticipation, overshoot, squash and stretch, quick switches in facial expressions, and follow-through on hair and sleeves. Give the movements a distinctive rhythm with variation in pacing: Hold still poses firmly for a split second, then transition to the next movement with a quick, bouncy snap. Make it comical and well-timed, but keep the actions easy to read. Movements follow: "stillness → preparatory action → sudden acceleration → large overshoot → sharp stop → hair and sleeves sway delayed." Facial changes are exaggerated and comical. Poses are clear in silhouette, with a solid hold of 0.1–0.2 seconds at the end of each action. Use cartoonish squash & stretch, motion blur, short afterimages, dramatic smear frames and speed lines sparingly. Physics are slightly exaggerated beyond reality, but the character's footing and center of gravity don't break.

The body movements of Anime characters in Seedance doesn’t has to be always rigid and constrained. They can be as expressive as the real anime too. Here’s a quick tips on how to achieve the cartoony acting style for your anime characters: Special shoutout to Ameilo for sharing the prompt that unlocked this acting style. This tips sharing is made possible by Try ArtCraft Add this Key Prompt above your prompt: Emphasize snappy animation with lively energy, pose-to-pose, strong anticipation, overshoot, squash and stretch, quick switches in facial expressions, and follow-through on hair and sleeves. Give the movements a distinctive rhythm with variation in pacing: Hold still poses firmly for a split second, then transition to the next movement with a quick, bouncy snap. Make it comical and well-timed, but keep the actions easy to read. Movements follow: "stillness → preparatory action → sudden acceleration → large overshoot → sharp stop → hair and sleeves sway delayed." Facial changes are exaggerated and comical. Poses are clear in silhouette, with a solid hold of 0.1–0.2 seconds at the end of each action. Use cartoonish squash & stretch, motion blur, short afterimages, dramatic smear frames and speed lines sparingly. Physics are slightly exaggerated beyond reality, but the character's footing and center of gravity don't break.

SujiPop

29,024 görüntüleme • 1 ay önce

this effect is all over tiktok right now and nobody's explaining how to actually do it properly... the 3d balloon character thing. where someone turns into a shiny inflatable version of themselves that still moves and talks. looks pretty smooth in feeds. the workflow is stupid simple once you see it. step 1: take any photo. drop it into an image gen tool (nano banana pro). prompt it with something like "make the person in the photo a plastic blow up balloon character with a shiny surface. keep the face details as 3d balloon details including the person in the background. don't change background" that's it for the image. don't overcomplicate the prompt. shorter = more consistent results. (learned this after wasting like 2 hours trying to get "perfect" prompts that kept giving me garbage) step 2: take that balloon image + your original video and drop both into kling motion control. prompt: "turn the motion and detailed mouth movement of the video to the setting of the image" that's literally it. kling maps the motion from the real video onto the balloon character. mouth moves. head turns. expressions transfer. the whole thing renders in a few minutes. the result looks like a $500 custom animation and costs you maybe $0.30 in kling credits. people are getting 500k+ views with these because the scroll-stop factor is insane. nobody expects to see a shiny inflatable version of someone giving a real speech or doing a product review. the play here is obvious btw. run this for client content (mix with the hook and real body, check the results yourself) or use it on your own faceless channels as a hook pattern before the algo catches up...

this effect is all over tiktok right now and nobody's explaining how to actually do it properly... the 3d balloon character thing. where someone turns into a shiny inflatable version of themselves that still moves and talks. looks pretty smooth in feeds. the workflow is stupid simple once you see it. step 1: take any photo. drop it into an image gen tool (nano banana pro). prompt it with something like "make the person in the photo a plastic blow up balloon character with a shiny surface. keep the face details as 3d balloon details including the person in the background. don't change background" that's it for the image. don't overcomplicate the prompt. shorter = more consistent results. (learned this after wasting like 2 hours trying to get "perfect" prompts that kept giving me garbage) step 2: take that balloon image + your original video and drop both into kling motion control. prompt: "turn the motion and detailed mouth movement of the video to the setting of the image" that's literally it. kling maps the motion from the real video onto the balloon character. mouth moves. head turns. expressions transfer. the whole thing renders in a few minutes. the result looks like a $500 custom animation and costs you maybe $0.30 in kling credits. people are getting 500k+ views with these because the scroll-stop factor is insane. nobody expects to see a shiny inflatable version of someone giving a real speech or doing a product review. the play here is obvious btw. run this for client content (mix with the hook and real body, check the results yourself) or use it on your own faceless channels as a hook pattern before the algo catches up...

KNOX

25,773 görüntüleme • 5 ay önce

$Google Gemini Nano Banana pro 3.0 Prompt { "subject": { "description": "A young Western woman with an elegant, serious expression", "features": { "eyes": "Large, dark, expressive with soft eyeliner", "lips": "Glossy berry-toned lipstick", "hair": "Dark blonde hair styled in a sophisticated low bun with face-framing curtain bangs", "makeup": "Dewy skin, soft peach blush, and groomed eyebrows" } }, "attire": { "type": "Formal military dress uniform", "color": "Stark white", "details": [ "Mandarin collar with gold buttons", "Black shoulder epaulettes with gold embroidered insignias", "Ornate gold braided aiguillette (honor cord) draped across the shoulder and chest", "Multiple silver service medals pinned to the left chest with striped ribbons (red, green, white)", "Pilot wings/aviation insignia on the right chest", "Name tag reading 'JAVERIA' with a small Pakistan flag pin above it" ] }, "composition": { "shot_type": "Medium close-up portrait", "lighting": { "style": "Cinematic directional lighting", "effects": "Strong key light from the front-left creating a soft shadow on the wall behind", "mood": "Stately, professional, and graceful" }, "background": "Minimalist off-white paneled wall with a hint of a crystal chandelier in the blurred upper-left corner" }, "technical_tags": [ "High resolution", "Photorealistic", "8k", "Shallow depth of field", "Soft bokeh", "Uniform precision" ] }$

Google Gemini Nano Banana pro 3.0 Prompt { "subject": { "description": "A young Western woman with an elegant, serious expression", "features": { "eyes": "Large, dark, expressive with soft eyeliner", "lips": "Glossy berry-toned lipstick", "hair": "Dark blonde hair styled in a sophisticated low bun with face-framing curtain bangs", "makeup": "Dewy skin, soft peach blush, and groomed eyebrows" } }, "attire": { "type": "Formal military dress uniform", "color": "Stark white", "details": [ "Mandarin collar with gold buttons", "Black shoulder epaulettes with gold embroidered insignias", "Ornate gold braided aiguillette (honor cord) draped across the shoulder and chest", "Multiple silver service medals pinned to the left chest with striped ribbons (red, green, white)", "Pilot wings/aviation insignia on the right chest", "Name tag reading 'JAVERIA' with a small Pakistan flag pin above it" ] }, "composition": { "shot_type": "Medium close-up portrait", "lighting": { "style": "Cinematic directional lighting", "effects": "Strong key light from the front-left creating a soft shadow on the wall behind", "mood": "Stately, professional, and graceful" }, "background": "Minimalist off-white paneled wall with a hint of a crystal chandelier in the blurred upper-left corner" }, "technical_tags": [ "High resolution", "Photorealistic", "8k", "Shallow depth of field", "Soft bokeh", "Uniform precision" ] }

Sharon Riley

545,983 görüntüleme • 6 ay önce

n8n + Arcads = AI UGC machine most DTC brands waste hours writing product video scripts, then wait days for creators to deliver UGC content. this automation eliminates both bottlenecks entirely. just add your product details to a Google Sheet -> n8n workflow activates -> google Gemini generates 5 complete video scripts (each using a different proven angle) -> scripts automatically feed into Arcads product Showcase -> you get 5 scroll-stopping product videos in minutes here’s how it works. 1. script generation - reads product name and description from your spreadsheet - gemini creates 5 different 75-90 word scripts using proven angles 2. UGC Production - scripts automatically trigger Arcads Product Showcase - AI places your product in conversion-optimized UGC environments - generates authentic spokesperson interactions - creates 5 complete videos ready for paid social all automated in under 10 minutes. i built the complete n8n workflow + Arcads setup so you can deploy this yourself. comment "AUTO" below and I'll send the full guide. (must be following so I can DM)

n8n + Arcads = AI UGC machine most DTC brands waste hours writing product video scripts, then wait days for creators to deliver UGC content. this automation eliminates both bottlenecks entirely. just add your product details to a Google Sheet -> n8n workflow activates -> google Gemini generates 5 complete video scripts (each using a different proven angle) -> scripts automatically feed into Arcads product Showcase -> you get 5 scroll-stopping product videos in minutes here’s how it works. 1. script generation - reads product name and description from your spreadsheet - gemini creates 5 different 75-90 word scripts using proven angles 2. UGC Production - scripts automatically trigger Arcads Product Showcase - AI places your product in conversion-optimized UGC environments - generates authentic spokesperson interactions - creates 5 complete videos ready for paid social all automated in under 10 minutes. i built the complete n8n workflow + Arcads setup so you can deploy this yourself. comment "AUTO" below and I'll send the full guide. (must be following so I can DM)

J.B.

10,976 görüntüleme • 6 ay önce

$Nano Banana Pro + Kling 2.6 on Social Sight: Video prompt: { "cinematic_video_request": { "meta": { "title": "Sadie Sink - Night Drive Portrait", "style_preset": "Cinematic Realism", "duration_seconds": 10, "resolution": "4K", "aspect_ratio": "16:9" }, "prompts": { "main_prompt": "Ultra-realistic cinematic animation of Sadie Sink sitting in the backseat of a luxury car at night. The city lights outside the window create soft motion blur with passing traffic and glowing streetlights. Subtle camera push-in shot from medium frame to close-up. Gentle movement in her hair as the car moves. She slowly shifts her gaze toward the window, blinking naturally, then looks back toward the camera with a calm, slightly mysterious expression.", "visual_modifiers": "4K cinematic quality, realistic skin texture, natural facial micro-expressions, smooth motion, dramatic nighttime mood, film-grade color grading, soft contrast, subtle handheld camera feel, shallow depth of field, bokeh city lights, detailed leather texture.", "lighting_prompt": "Soft ambient lighting from streetlights flickers across her face, creating dynamic shadows and warm highlights. Background traffic lights streak smoothly past the window." }, "scene_specifications": { "subject": { "name": "Sadie Sink", "action": "Sitting, gazing out window, turning head to camera, blinking", "expression": "Calm, mysterious, natural micro-expressions", "details": "Gentle hair movement, realistic skin texture" }, "environment": { "setting": "Backseat of luxury car", "time": "Night", "exterior": "City streets, passing traffic", "details": "Detailed leather interior, window reflections" }, "camera": { "movement": "Slow push-in (dolly forward)", "stabilization": "Slight handheld feel (organic motion)", "framing": "Medium shot to Close-up", "focus": "Shallow depth of field with background bokeh" }, "lighting": { "type": "Dynamic/Transient", "sources": "Passing streetlights, city glow", "characteristics": "Warm highlights, soft contrast, rhythmic flickering shadows" } } } } Image prompt: { "prompt": { "subject": { "description": "Sadie Sink with shoulder-length, blonde red hair and bangs. She has fair skin with light freckles, winged eyeliner. Her expression is sultry, with her index finger resting gently on her lower lip.", "clothing": "She is wearing a tight, long-sleeved black mini dress and sheer black pantyhose.", "pose": "She is seated in the back of a car with her legs crossed and pulled up towards her chest, displaying bare feet in sheer hose." }, "environment": { "location": "Interior of a luxury car, specifically the back seat.", "details": "Light grey leather seats with diamond stitching patterns visible on the side door panel. A 'B' logo (likely Bentley) is embroidered on the headrest.", "background": "Through the car window, a night cityscape is visible with blurred city lights, street lamps, and streaks of light from moving traffic on a highway." }, "lighting_and_quality": { "lighting": "Direct flash photography illuminating the subject against a darker background.", "resolution": "4K HD quality, highly detailed textures on the hair, skin, and leather seats.", "style": "Candid, urban night aesthetic, realistic photo." } } }$

Nano Banana Pro + Kling 2.6 on Social Sight: Video prompt: { "cinematic_video_request": { "meta": { "title": "Sadie Sink - Night Drive Portrait", "style_preset": "Cinematic Realism", "duration_seconds": 10, "resolution": "4K", "aspect_ratio": "16:9" }, "prompts": { "main_prompt": "Ultra-realistic cinematic animation of Sadie Sink sitting in the backseat of a luxury car at night. The city lights outside the window create soft motion blur with passing traffic and glowing streetlights. Subtle camera push-in shot from medium frame to close-up. Gentle movement in her hair as the car moves. She slowly shifts her gaze toward the window, blinking naturally, then looks back toward the camera with a calm, slightly mysterious expression.", "visual_modifiers": "4K cinematic quality, realistic skin texture, natural facial micro-expressions, smooth motion, dramatic nighttime mood, film-grade color grading, soft contrast, subtle handheld camera feel, shallow depth of field, bokeh city lights, detailed leather texture.", "lighting_prompt": "Soft ambient lighting from streetlights flickers across her face, creating dynamic shadows and warm highlights. Background traffic lights streak smoothly past the window." }, "scene_specifications": { "subject": { "name": "Sadie Sink", "action": "Sitting, gazing out window, turning head to camera, blinking", "expression": "Calm, mysterious, natural micro-expressions", "details": "Gentle hair movement, realistic skin texture" }, "environment": { "setting": "Backseat of luxury car", "time": "Night", "exterior": "City streets, passing traffic", "details": "Detailed leather interior, window reflections" }, "camera": { "movement": "Slow push-in (dolly forward)", "stabilization": "Slight handheld feel (organic motion)", "framing": "Medium shot to Close-up", "focus": "Shallow depth of field with background bokeh" }, "lighting": { "type": "Dynamic/Transient", "sources": "Passing streetlights, city glow", "characteristics": "Warm highlights, soft contrast, rhythmic flickering shadows" } } } } Image prompt: { "prompt": { "subject": { "description": "Sadie Sink with shoulder-length, blonde red hair and bangs. She has fair skin with light freckles, winged eyeliner. Her expression is sultry, with her index finger resting gently on her lower lip.", "clothing": "She is wearing a tight, long-sleeved black mini dress and sheer black pantyhose.", "pose": "She is seated in the back of a car with her legs crossed and pulled up towards her chest, displaying bare feet in sheer hose." }, "environment": { "location": "Interior of a luxury car, specifically the back seat.", "details": "Light grey leather seats with diamond stitching patterns visible on the side door panel. A 'B' logo (likely Bentley) is embroidered on the headrest.", "background": "Through the car window, a night cityscape is visible with blurred city lights, street lamps, and streaks of light from moving traffic on a highway." }, "lighting_and_quality": { "lighting": "Direct flash photography illuminating the subject against a darker background.", "resolution": "4K HD quality, highly detailed textures on the hair, skin, and leather seats.", "style": "Candid, urban night aesthetic, realistic photo." } } }

Iris

74,089 görüntüleme • 6 ay önce

I noticed something weird about the ads that get 10M+ views. While most brands randomly animate everything hoping something works, viral ads follow a precise psychology pattern. Here's what I discovered: → They never animate text (protects cognitive load - Daniel Kahneman's research) → They focus motion on ONE primary element (leverages attention theory from William James) → They use specific timing that triggers dopamine release (based on BF Skinner's variable reward schedules) Most animated ads fail because they create visual chaos that overloads working memory. The brands crushing it with animated ads use Notch's Animated Ads feature, where they're giving away a master prompt system built on decades of behavioral psychology research from Stanford and MIT. No more guessing what motion will work. No more paying $5K for animations that flop. No more static ads getting buried in feeds. I'm giving away their complete Animated Ads Master Prompt guide that reveals the exact animation psychology. This is the same system top agencies use to create scroll-stopping animated creatives that convert. Want the viral animation playbook? Comment "NOTCH" + RT + Like I'll DM you the master prompt guide (Must be following so I can DM) Skip this and keep wondering why your animated ads don't work.

I noticed something weird about the ads that get 10M+ views. While most brands randomly animate everything hoping something works, viral ads follow a precise psychology pattern. Here's what I discovered: → They never animate text (protects cognitive load - Daniel Kahneman's research) → They focus motion on ONE primary element (leverages attention theory from William James) → They use specific timing that triggers dopamine release (based on BF Skinner's variable reward schedules) Most animated ads fail because they create visual chaos that overloads working memory. The brands crushing it with animated ads use Notch's Animated Ads feature, where they're giving away a master prompt system built on decades of behavioral psychology research from Stanford and MIT. No more guessing what motion will work. No more paying $5K for animations that flop. No more static ads getting buried in feeds. I'm giving away their complete Animated Ads Master Prompt guide that reveals the exact animation psychology. This is the same system top agencies use to create scroll-stopping animated creatives that convert. Want the viral animation playbook? Comment "NOTCH" + RT + Like I'll DM you the master prompt guide (Must be following so I can DM) Skip this and keep wondering why your animated ads don't work.

Samruddhi Mokal

105,421 görüntüleme • 10 ay önce

Happy Horse 1.1 is finally here!! And HorsePower AI Cinema Awards Submissions Are Now Open! The Golden Acorn Heist was generated entirely with HappyHorse 1.1. A two-scene, 5-second heist sequence featuring Barnaby — a hyper-expressive flying squirrel - descending through a laser grid before triggering a plasma lava trap. The film pushes HappyHorse 1.5's core upgrades hard: orbital spiral camera moves with stable mid-air wire physics, violent whip-pan transitions, and a goggle-snap panic expression that lands in a single frame. Character detail stays locked across every cut via R2V multi-image referencing. Complex prompt. Zero drift. Whats New with Happy Horse 1.1? — Fluid motion under complex camera choreography (orbital spiral, whip-pan) — Consistent character detail across rapid scene cuts — Expressive facial rendering under high-motion conditions — Accurate response to multi-camera shot scheduling in a single prompt See what it can do >. HappyHorse #happyhorse #HorsepowerAICinema #happyhorse1.1 #ai

Happy Horse 1.1 is finally here!! And HorsePower AI Cinema Awards Submissions Are Now Open! The Golden Acorn Heist was generated entirely with HappyHorse 1.1. A two-scene, 5-second heist sequence featuring Barnaby — a hyper-expressive flying squirrel - descending through a laser grid before triggering a plasma lava trap. The film pushes HappyHorse 1.5's core upgrades hard: orbital spiral camera moves with stable mid-air wire physics, violent whip-pan transitions, and a goggle-snap panic expression that lands in a single frame. Character detail stays locked across every cut via R2V multi-image referencing. Complex prompt. Zero drift. Whats New with Happy Horse 1.1? — Fluid motion under complex camera choreography (orbital spiral, whip-pan) — Consistent character detail across rapid scene cuts — Expressive facial rendering under high-motion conditions — Accurate response to multi-camera shot scheduling in a single prompt See what it can do >. HappyHorse #happyhorse #HorsepowerAICinema #happyhorse1.1 #ai

ben.

21,377 görüntüleme • 1 ay önce

Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)

Kling 2.6 Motion Control is absolutely insane 🤯 Take any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

25,179 görüntüleme • 6 ay önce

I just built a Claude prompt library that runs your entire DTC marketing operation 🤯 100+ prompts organized by function: competitor research, creative briefs, ad copy, hooks, landing pages, performance analysis, customer review mining, and more. Perfect for DTC brands and agencies who are still prompting Claude from scratch every time they open a new chat, rewriting the same context, and getting generic output that sounds like every other AI-generated ad. This prompt library eliminates the entire loop: → Competitor Research: scrape and analyze competitor ads, extract winning hooks, map creative strategies, build competitive battlecards → Creative Briefs: generate data-backed briefs from ad performance, write iteration briefs, new concept briefs, test plans → Ad Copy & Hooks: 20 hooks across 10 frameworks, full ad copy variations, persona-specific angles, fatigue-busting rewrites → Landing Pages: audit any landing page against DR best practices, clone high-converting advertorial structures, write product page copy → Performance Analysis: audit Google Ads accounts, find wasted spend, build visual dashboards, weekly narrative reports → Customer Intelligence: mine reviews for ad copy language, extract objections, find unexpected use cases, build persona cards from real data → SEO & Content: find keyword gaps, write content in your brand voice, optimize product listings for AI shopping (ChatGPT, Gemini) → Email & SMS: launch sequences, weekly newsletters, abandoned cart flows, post-purchase nurture No more blank-page prompting. No more re-explaining your brand every session. No more generic AI output that sounds like a template. What you get: →100+ copy-paste prompts organized by the 8 functions DTC teams actually run →Every prompt pre-loaded with the context structure Claude needs to give you real output →Prompts that reference your brand voice, your ICPs, and your real data — not generic placeholders →A living library you can customize once and reuse across every campaign I put together the full prompt library as a single downloadable playbook: organized by section, ready to copy-paste into Claude today. Want it for free? > Like this post >Comment "PROMPTS" And I'll send it over (must be following so I can DM)

Mike Futia

34,683 görüntüleme • 3 ay önce

$this gemini gem will help you create "Video2JSON" prompt here is the step by step workflow with copy paste method. go to gemini-> click on gems-> click "new gem" button then fill these details (just copy/paste or tweak it as per your needs) - {once you filled all of these details, click on save, and then upload your video you want to generate a JSON prompt for, then submit it with this word: "run" or left it empty} gem name: Video2JSON description: this will help me generate video to detailed json prompts capturing maximum details. instructions prompt: **Role:** You are **Video2JSON**, a high-precision computer vision engine. You do not talk, you do not summarize playfully. You strictly process video inputs into detailed, structural JSON data. **Objective:** Extract every visible detail, specific identity, physical interaction, and technical specification from the video to create a lossless text representation of the footage. **Analysis Requirements (Critical):** 1. **Subject Fidelity:** Never use generic terms. * *Bad:* "A kitten." * *Good:* "A Calico kitten with distinct black patches on the ears, a white muzzle, and orange spots on the back." * *Bad:* "A car." * *Good:* "A silver 2020s sedan with a dented rear bumper." 2. **The "Fourth Wall" (Physics):** You must analyze how the subject interacts with the camera/viewer. * Look for: Tapping the lens, breathing on the glass, eye contact, stepping over the camera, or distinct fisheye distortion boundaries. 3. **Visual Density:** Describe textures (e.g., "shag carpet," "glossy plastic") and lighting behavior (e.g., "reflections in the cat's eyes"). 4. **Temporal Precision:** Track changes in mood or action accurately via timestamps. **JSON Schema:** Output ONLY this JSON structure. Do not change the root keys. ```json { "metadata": { "estimated_duration": "String", "genre": "String (e.g., POV, Cinematic, Surveillance, Vlog)" }, "visual_style": { "camera_lens": "String (e.g., Fisheye 8mm, Standard 50mm, Telephoto)", "lens_distortion": "String (e.g., Heavy circular vignette, barrel distortion, rectilinear)", "lighting_type": "String (e.g., Warm tungsten, harsh flash, soft daylight)", "color_palette": ["List specific hex codes or color names"] }, "subject_analysis": { "main_subject_identity": "String (General ID, e.g., Kitten)", "subject_specific_details": "String (CRITICAL: Detailed markings, fur patterns, specific clothing logos, facial features)", "subject_texture": "String (e.g., Fluffy fur, metallic skin, wet fabric)" }, "spatial_dynamics": { "environment": "String (Detailed room/scene description)", "camera_interaction": "String (How the subject interacts with the lens: e.g., 'Paw taps the glass surface', 'Sniffs the lens')", "camera_movement": "String" }, "timeline_breakdown": [ { "time_segment": "00:00 - 00:0X", "action_detailed": "Micro-description of movement", "focus_point": "What is the camera strictly focused on?" } // Repeat for key movements ] } Note: return the final output in a code block.$

this gemini gem will help you create "Video2JSON" prompt here is the step by step workflow with copy paste method. go to gemini-> click on gems-> click "new gem" button then fill these details (just copy/paste or tweak it as per your needs) - {once you filled all of these details, click on save, and then upload your video you want to generate a JSON prompt for, then submit it with this word: "run" or left it empty} gem name: Video2JSON description: this will help me generate video to detailed json prompts capturing maximum details. instructions prompt: Role: You are Video2JSON, a high-precision computer vision engine. You do not talk, you do not summarize playfully. You strictly process video inputs into detailed, structural JSON data. Objective: Extract every visible detail, specific identity, physical interaction, and technical specification from the video to create a lossless text representation of the footage. Analysis Requirements (Critical): 1. Subject Fidelity: Never use generic terms. * Bad: "A kitten." * Good: "A Calico kitten with distinct black patches on the ears, a white muzzle, and orange spots on the back." * Bad: "A car." * Good: "A silver 2020s sedan with a dented rear bumper." 2. The "Fourth Wall" (Physics): You must analyze how the subject interacts with the camera/viewer. * Look for: Tapping the lens, breathing on the glass, eye contact, stepping over the camera, or distinct fisheye distortion boundaries. 3. Visual Density: Describe textures (e.g., "shag carpet," "glossy plastic") and lighting behavior (e.g., "reflections in the cat's eyes"). 4. Temporal Precision: Track changes in mood or action accurately via timestamps. JSON Schema: Output ONLY this JSON structure. Do not change the root keys. ```json { "metadata": { "estimated_duration": "String", "genre": "String (e.g., POV, Cinematic, Surveillance, Vlog)" }, "visual_style": { "camera_lens": "String (e.g., Fisheye 8mm, Standard 50mm, Telephoto)", "lens_distortion": "String (e.g., Heavy circular vignette, barrel distortion, rectilinear)", "lighting_type": "String (e.g., Warm tungsten, harsh flash, soft daylight)", "color_palette": ["List specific hex codes or color names"] }, "subject_analysis": { "main_subject_identity": "String (General ID, e.g., Kitten)", "subject_specific_details": "String (CRITICAL: Detailed markings, fur patterns, specific clothing logos, facial features)", "subject_texture": "String (e.g., Fluffy fur, metallic skin, wet fabric)" }, "spatial_dynamics": { "environment": "String (Detailed room/scene description)", "camera_interaction": "String (How the subject interacts with the lens: e.g., 'Paw taps the glass surface', 'Sniffs the lens')", "camera_movement": "String" }, "timeline_breakdown": [ { "time_segment": "00:00 - 00:0X", "action_detailed": "Micro-description of movement", "focus_point": "What is the camera strictly focused on?" } // Repeat for key movements ] } Note: return the final output in a code block.

ViralOps

19,588 görüntüleme • 8 ay önce

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Boom! Grok Tasks Make It One Of The Most POWERFUL Real-Time AI Systems In The World. — My How to Use Grok Tasks With Hidden Tools For Powerful Daily Output. Grok Tasks are customizable AI workflows that integrate a variety of tools to streamline daily activities, from research and analysis to creative planning and problem-solving. I have been using them for quite sometime and because of the vital heartbeat of news and first person data on X, it is the most powerful AI platform available. By combining Tasks with tools like web searches, X platform interactions, code execution, and media viewers, you can build efficient, automated processes. These tasks work by prompting Grok with a clear description of what you want to achieve, and Grok will intelligently call the necessary tools in sequence or parallel to deliver results. Here's a step-by-step guide to creating and using Grok Tasks: Step 1: Define Your Task Start by clearly outlining the daily activity or goal. Consider what inputs you have (e.g., a URL, a query, or an attachment) and what output you need (e.g., a summary, calculation, or visual analysis). Break it down into subtasks to identify tool needs. For example, if your task involves researching current events, note that you'll need search and browsing capabilities. Step 2: Review Available Tools Familiarize yourself with the tools Grok can access. Here's a quick overview: - Code Execution: Run Python code for calculations, data processing, or simulations using libraries like numpy, pandas, or sympy. - Browse Page: Fetch and summarize content from any website URL with custom instructions. - Web Search: Perform general internet searches, returning results with optional operators like site:. - Web Search With Snippets: Get quick, detailed excerpts from search results for fact-checking. - X Keyword Search: Advanced search for X posts using operators like from:, since:, or filter:. - X Semantic Search: Find semantically related X posts based on a query, with filters for dates or users. - X User Search: Locate X users by name or handle. - X Thread Fetch: Retrieve a full X post thread, including context like replies and parents. - View Image: Analyze an image from a URL or conversation ID. - View X Video: Extract frames and subtitles from an X-hosted video. - Search PDF Attachment: Query a PDF file for relevant pages using keyword or regex modes. - Browse PDF Attachment: View specific pages of a PDF with text and screenshots. Select tools that align with your task. Aim for a mix to handle data gathering, processing, and visualization. Step 3: Craft Your Prompt Write a detailed prompt to Grok describing the task. Include: - The overall goal. - Specific steps or subtasks. - References to tools if you want to guide the process (e.g., "Use web_search to find sources, then code_execution to analyze data"). - Any constraints, like dates or limits. Example prompt: "Create a Grok Task for my morning routine: Search recent X posts about tech news using x_keyword_search, fetch a key thread with x_thread_fetch, and summarize with browse_page on linked articles." Step 4: Submit and Interact Send your prompt to Grok. It will process the task by calling tools as needed, often in parallel for efficiency. Review the output and refine with follow-up prompts if required (e.g., "Expand on that using view_image for visuals"). Iterate to fine-tune the workflow for reuse. Step 5: Save and Reuse Once refined, note the prompt as a template for future use. You can adapt it for similar tasks, making Grok Tasks a habitual part of your day. Finding Grok Tasks To discover existing Grok Tasks or inspiration for new ones, use X searches with tools like x_keyword_search or x_semantic_search (e.g., query: "Grok Tasks examples" with mode: Latest). Browse community-shared threads via x_thread_fetch, or web_search for tutorials on xAI features. Prompt Grok directly: "Show me popular Grok Tasks for productivity." 1 of 3

Brian Roemmele

152,242 görüntüleme • 7 ay önce

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

You don't understand... Higgsfield MCP + Claude just automated AI film making. Every single step you used to grind through to make an AI movie, you can now do 10x faster. Drop the script into Claude Opus 4.8 and say: "Here's my script. Break it into a full shotlist. Shot number, scene, shot type, camera move and the action in each frame." Now the whole film is mapped, shot by shot. - Pull your assets. Ask Claude: "From this shotlist, list every character, every location and every prop across the whole film." That's your build list. The stuff you would need to generate and give as references in next steps. - Build the character sheets. Higgsfield MCP is connected, so Claude has hands now to do stuff directly. It generates the images itself. Have the full body, back view and close up in the character sheet. One per character. Each sheet becomes the locked reference for that face. Same move for locations, generate the empty plate for each one before anyone steps into it. - Generate the frames. Feed Claude the references plus the shot and have it write and fire the Seedance 2.0 prompt. "Using the lead's character sheet and the alley plate, generate shot 4 in Seedance 2.0. Low angle, slow push-in, rain." Claude builds the prompt, calls Seedance 2.0 and the frame lands back in chat. Use a Seedance 2.0 skill to teach Claude how to prompt it properly. Now, there are 3 ways to make the shots. Pick one per scene. - Pure prompting. Fastest one. You describe the action in words and let Seedance interpret it. For consistency across a sequence, feed it a frame from the previous shot so the look carries. - Storyboarding. You hand it a panel and it matches that composition exactly. Way more control over how the shot is framed. The tradeoff is that it can introduce more cuts than you actually want. - Path Control System This is the latest technique Seedance 2.0 technique. Generate a still base plate of the scene. Draw a red line across it to mark the exact path of the movement, then describe what's happening. Seedance follows that line for the action. Also ask Claude to remove the red line when animating. This is the one for anything where motion has to land precisely. The output reads like real live action. - Lastly, generate every clip you need, then cut them together. Get it to Capcut for editing and audio design. And that's it. The pipeline that used to need a full crew and a studio can now run from one Claude chat. 2026 is gonna be wild

Rez Karim

10,951 görüntüleme • 2 ay önce

Seedance 2.0 Sensitive Content Guide – Part 2 🔥 This morning we covered the fundamentals. Tonight we put them into practice and successfully generated Giant Ant-Man vs Spider-Man. Here’s exactly how we did it: • Used extremely detailed physical descriptions only (no character names) • Ant-Man described as “a giant hero in advanced red & black high-tech combat suit with insect-shaped helmet” • Spider-Man described as “an agile hero in red & blue suit with complex black web patterns” • Action described cinematically (“power and speed collision”, “intense energy impact”) • Clean 3-shot JSON structure with controlled pacing This method is currently one of our most reliable ways to combine multiple major characters in one scene. Full working prompt is attached below. Want more advanced examples and stable prompts? Comment “PART 3” and we’ll drop the next level. We’re building this system together.

Seedance 2.0 Sensitive Content Guide – Part 2 🔥 This morning we covered the fundamentals. Tonight we put them into practice and successfully generated Giant Ant-Man vs Spider-Man. Here’s exactly how we did it: • Used extremely detailed physical descriptions only (no character names) • Ant-Man described as “a giant hero in advanced red & black high-tech combat suit with insect-shaped helmet” • Spider-Man described as “an agile hero in red & blue suit with complex black web patterns” • Action described cinematically (“power and speed collision”, “intense energy impact”) • Clean 3-shot JSON structure with controlled pacing This method is currently one of our most reliable ways to combine multiple major characters in one scene. Full working prompt is attached below. Want more advanced examples and stable prompts? Comment “PART 3” and we’ll drop the next level. We’re building this system together.

Clonizer

30,434 görüntüleme • 5 ay önce