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📖 MOST CREATORS PROMPT SEEDANCE 2.0 WRONG — HERE'S THE JSON METHOD THAT FIXES IT This workflow takes about 12 minutes and most creators are still skipping it. Here's the exact pipeline: Generate the base frame in ChatGPT Image — keep the prompt specific: subject, lighting, camera distance, mood... Drop that image into Seedance 2.0 as the reference frame Use a structured JSON prompt instead of a plain text description — Seedance reads structured data far more consistently than freeform sentences Define camera movement, duration, and motion intensity as separate fields, not buried in a paragraph Example JSON prompt: { "scene": "vast sky realm with floating stone islands and waterfalls pouring into clouds below", "subject": "lone hooded figure in weathered cloak and armor, standing on a cliff edge", "action": "raises both arms to open a glowing circular portal etched with ancient symbols", "camera_movement": "slow push in, low angle tilting up toward the portal", "duration": "6s", "motion_intensity": "medium", "environment": "floating islands suspended in golden haze, twin waterfalls cascading into a sea of clouds", "lighting": "warm backlit sun, golden hour haze, glowing rim light on the figure", "effects": "swirling energy ring, light particles streaming upward, faint lens flare", "color_palette": "teal sky, amber light, desaturated stone tones", "mood": "epic, mystical, ancient power awakening" } Feed that JSON straight into Seedance 2.0's prompt field Render and review — if motion feels off, adjust motion_intensity first, that field controls most of the result Stack 3-4 clips with matching lighting fields for a consistent short sequence The reason JSON beats plain text here: each field is isolated, so the model doesn't have to guess which word controls what. Camera behavior becomes predictable instead of random. This is also where the monetization angle gets interesting — brands paying for short cinematic ads care less about the prompt method and more about consistency across multiple clips. JSON prompting is what makes that consistency repeatable at scale. ~ The video was created by Aastha ~ 📥Tomorrow I will reveal something so underrated it almost did not make it into a post. 🔖Every step of this workflow is documented in the pinned article below.show more

Zentrix⌚️

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📖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⌚️

14,015 просмотров • 3 месяцев назад

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

NOVA

64,180 просмотров • 6 месяцев назад

Claude Code + Nano Banana 2 is f*cking cracked 🤯 I built a skill inside Claude Code that writes JSON image prompts for Nano Banana 2, and the outputs look like they came from a professional photo shoot. One plain-text prompt. Claude rewrites it as structured JSON with lighting, camera, composition, style, and negative prompts. Then fires it off to Nano Banana 2. All inside Claude Code. Perfect for DTC brands and agencies who need high-volume ad creative without booking a shoot. If you're using Nano Banana 2 for product shots and lifestyle images but every generation feels like pulling a slot machine lever — random lighting, inconsistent style, plastic skin, misspelled labels ... This skill fixes the entire output: → You describe what you want in plain English → Claude rewrites it as a structured JSON prompt (lighting, camera angle, lens, depth of field, color grading — all of it) → Fires it to Nano Banana 2 via API → Saves the prompt + image in organized folders → You iterate on the style until it's dialed, then every output matches No more slot machine prompting. No more inconsistent brand imagery. No more burning credits on unusable generations. What you get: - Photo-realistic product shots and lifestyle images on demand - Full control over style, lighting, composition, and camera settings - Saved JSON prompts you can reuse across every campaign - A skill that gets smarter the more feedback you give it Built 100% in Claude Code with a custom skill + Python scripts. I put together a full playbook showing the exact skill, the JSON schema, and the workflow to set this up yourself. Want the full playbook? > Like this post > Comment "BANANA" And I'll send it over (must be following so I can DM)

Mike Futia

212,556 просмотров • 7 месяцев назад

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Beauty ads just changed forever. Free Claude Opus 4.8 + GPT Image 2 + Seedance 2.0 workflow to spin up 100s of video ads. No studio, no model, no macro lens, no shoot day. Here's what nobody in beauty marketing wants to say out loud. That glossy lip shot. The droplet hitting the surface in slow motion. The whip-pan into the next scene. The crystalline product splash. All the stuff that used to need a real set, a real camera op, and a full shoot day. You can generate every frame of it from a text prompt now, and stitch it into a finished ad before your coffee goes cold. The workflow is almost stupidly simple: → Tell Claude Opus 4.8 the beauty shot you want (dewy skin macro, gloss-on-lips contact, ripple transition, the works) → Claude turns it into a shot-by-shot storyboard plus a prompt for every frame → GPT Image 2 generates the photoreal stills, frame by frame → Seedance 2.0 animates each one into a clip with that buttery slow-mo glide → You drop the clips into HeyOz and assemble the full ad in one place The real unlock is volume. This isn't one hero video. Once the workflow is dialed, you spin up hundreds of variations. Different shades, different models, different hooks, different transitions. The exact creative volume Meta rewards, minus the production cost that used to make it impossible. Old way: one shoot, one look, $10k+, weeks of waiting. New way: a hundred angles, any look, a few dollars each, same afternoon. I wrote up the entire workflow. The Claude storyboard prompt, the GPT Image 2 frame prompts, the Seedance motion settings, the full assembly flow. Completely free, no email gate. Want it? Comment "GLOSS" and I'll send it straight over. (make sure you're following so it can actually reach you)

Ahad Shams | AI Ads Guy

11,303 просмотров • 3 месяцев назад

Seedance 2.0 + Claude Code is f*cking insane 🤯 I built a Claude skill that creates UGC ads on demand. One product + one prompt = the AI creator, the script, the scene-by-scene shot list, and the finished video. All inside Claude Code. Perfect for DTC brands and agencies who can't afford to keep paying $500-$1,500 per UGC video and waiting 2 weeks for revisions. This skill eliminates the entire loop: → Tell Claude the product, ad angle, and length → Skill writes the GPT Image 2.0 prompt to generate the AI creator from scratch → Skill writes every scene prompt, dialogue line, and delivery direction → Pipes it into Seedance 2.0 with character + product + voice locked → Speed up + caption in CapCut → Ship the ad in 20 minutes No more paying $11 per video on Arcads. No more 2-week revision cycles. No more PR boxes to creators who ghost you. What you get: → Perfect character consistency across every scene → Voice consistency that holds clip-to-clip → Real product fidelity using your actual product photo as a reference → Multi-scene day-in-life, testimonial, and action-shot formats out of the box Built 100% with a Claude skill + Seedance 2.0. I recorded a full step-by-step tutorial showing the exact workflow so you can build these AI UGC ads yourself. Want the full breakdown? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)

Seedance 2.0 + Claude Code is f*cking insane 🤯 I built a Claude skill that creates UGC ads on demand. One product + one prompt = the AI creator, the script, the scene-by-scene shot list, and the finished video. All inside Claude Code. Perfect for DTC brands and agencies who can't afford to keep paying $500-$1,500 per UGC video and waiting 2 weeks for revisions. This skill eliminates the entire loop: → Tell Claude the product, ad angle, and length → Skill writes the GPT Image 2.0 prompt to generate the AI creator from scratch → Skill writes every scene prompt, dialogue line, and delivery direction → Pipes it into Seedance 2.0 with character + product + voice locked → Speed up + caption in CapCut → Ship the ad in 20 minutes No more paying $11 per video on Arcads. No more 2-week revision cycles. No more PR boxes to creators who ghost you. What you get: → Perfect character consistency across every scene → Voice consistency that holds clip-to-clip → Real product fidelity using your actual product photo as a reference → Multi-scene day-in-life, testimonial, and action-shot formats out of the box Built 100% with a Claude skill + Seedance 2.0. I recorded a full step-by-step tutorial showing the exact workflow so you can build these AI UGC ads yourself. Want the full breakdown? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)

Mike Futia

41,604 просмотров • 4 месяцев назад

🚨 AI VIDEO PROMPTING IS DEAD !! You no longer need to write a 500-word prompt just to tell AI how your video should look. 💀 I just tested Pippit’s 3D Director Studio + Seedance 2.5 And instead of explaining the entire scene with words… I literally BUILT the scene first. For my test, I created a cinematic fight scene with characters inside a 3D environment. I could control: 1️⃣ Where the characters stood 2️⃣ Which props they held 3️⃣ Where the camera was placed 4️⃣ What each character should do Then I could actually CHOREOGRAPH the action. → Run → Fight → Shoot → Fall → React → Move You can place these actions on the timeline, adjust when they happen, and even plan the camera movement and character trajectories. So instead of: PROMPT → GENERATE → PRAY 😂 It becomes: BUILD → DIRECT → GENERATE And that gives you WAY more control. Because you’re not asking AI to imagine the entire scene from a block of text. You’re literally showing it the: → Characters → Environment → Props → Positions → Actions → Camera Then Seedance 2.5 takes that 3D direction and turns it into the final video. That’s what makes this so interesting. It feels less like prompting an AI video model… And more like sitting in the DIRECTOR’S CHAIR. 🎬 Try Pippit: Pippitofficial #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

🚨 AI VIDEO PROMPTING IS DEAD !! You no longer need to write a 500-word prompt just to tell AI how your video should look. 💀 I just tested Pippit’s 3D Director Studio + Seedance 2.5 And instead of explaining the entire scene with words… I literally BUILT the scene first. For my test, I created a cinematic fight scene with characters inside a 3D environment. I could control: 1️⃣ Where the characters stood 2️⃣ Which props they held 3️⃣ Where the camera was placed 4️⃣ What each character should do Then I could actually CHOREOGRAPH the action. → Run → Fight → Shoot → Fall → React → Move You can place these actions on the timeline, adjust when they happen, and even plan the camera movement and character trajectories. So instead of: PROMPT → GENERATE → PRAY 😂 It becomes: BUILD → DIRECT → GENERATE And that gives you WAY more control. Because you’re not asking AI to imagine the entire scene from a block of text. You’re literally showing it the: → Characters → Environment → Props → Positions → Actions → Camera Then Seedance 2.5 takes that 3D direction and turns it into the final video. That’s what makes this so interesting. It feels less like prompting an AI video model… And more like sitting in the DIRECTOR’S CHAIR. 🎬 Try Pippit: Pippitofficial #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

SANI BULA

95,462 просмотров • 22 дней назад

i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:

i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:

Machina

61,554 просмотров • 1 месяц назад

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

This is not camera footage. It is a Blender character with 8K skin, detailed wrinkles, wet eyes, facial controls and enough micro-detail to make your brain keep waiting for the person to behave like a person. HumanPro packages that skin workflow into a Blender add-on instead of making artists rebuild it from scratch every time. The interesting AI angle is not “AI made a realistic girl.” A reusable 3D human can keep the same face across thousands of shots, then be relit, reposed, animated and dropped into completely different scenes without the identity drifting every six frames. Add Claude through Blender MCP and the workflow gets stranger: the model can help assemble scenes, adjust cameras and lighting, inspect renders and correct obvious visual problems, while the character system handles the skin and facial structure. It still does not remove the artist. Someone has to control expression, motion, lighting and the exact moment realism quietly turns into a very expensive mannequin. Most AI influencer projects are still fighting prompt consistency one image at a time. A rigged digital human is less magical, but probably much closer to how this becomes an actual production system.

Rina

109,872 просмотров • 2 месяцев назад

$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

19,659 просмотров • 9 месяцев назад

What if you could choreograph an entire action scene before generating a single frame? 🥋🎬 That’s what I wanted to test with Pippitofficial’s 3D Director Studio + Seedance 2.5. I created a scene with two girls demonstrating a series of karate moves and used the 3D workspace to figure out the sequence first. The interesting part is that I wasn’t relying on a giant prompt to explain every movement. I could place both characters in the scene, set up the environment, choose props from 150+ available options, and then map out how the performance should unfold. One move at a time. Step in → stance → punch → block → kick → reset → next move. The timeline makes it possible to arrange actions and control their timing, while movement trajectories help define where the characters should go. And then there’s the camera. Instead of hoping the generated video finds the right angle, I could plan the camera movement around the choreography and decide how the sequence should be captured. That’s the part I found most useful. You can think through the scene visually before asking AI to generate the final result. Characters. Movement. Timing. Camera. All planned first. Then Seedance 2.5 turns that setup into the actual video. For me, this feels like a shift from simply prompting AI to actually directing the shot. Try Pippit 3D Director Studio: #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

What if you could choreograph an entire action scene before generating a single frame? 🥋🎬 That’s what I wanted to test with Pippitofficial’s 3D Director Studio + Seedance 2.5. I created a scene with two girls demonstrating a series of karate moves and used the 3D workspace to figure out the sequence first. The interesting part is that I wasn’t relying on a giant prompt to explain every movement. I could place both characters in the scene, set up the environment, choose props from 150+ available options, and then map out how the performance should unfold. One move at a time. Step in → stance → punch → block → kick → reset → next move. The timeline makes it possible to arrange actions and control their timing, while movement trajectories help define where the characters should go. And then there’s the camera. Instead of hoping the generated video finds the right angle, I could plan the camera movement around the choreography and decide how the sequence should be captured. That’s the part I found most useful. You can think through the scene visually before asking AI to generate the final result. Characters. Movement. Timing. Camera. All planned first. Then Seedance 2.5 turns that setup into the actual video. For me, this feels like a shift from simply prompting AI to actually directing the shot. Try Pippit 3D Director Studio: #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

Shabnam Parveen

51,749 просмотров • 23 дней назад

From chaos to confidence, one spark changed everything. Sometimes your greatest power is the courage to transform. Created this 13-second on using Seedance 2.0 on WaveSpeedAI Prompt: A cinematic action sequence featuring a confident young woman with auburn hair tied in a ponytail, wearing a black leather jacket, dark pants, and combat boots. She runs through a ruined city street filled with smoke, debris, abandoned vehicles, and attacking zombie-like creatures. The camera follows with dynamic handheld tracking shots and dramatic slow motion. As enemies surround her, she stops, raises her arms, and a brilliant golden energy erupts from her body, forming glowing rings and powerful light waves that blast the monsters away. The destruction instantly transforms into a luxurious modern shopping boulevard with elegant boutiques, warm golden-hour sunlight, clean streets, and fashionable pedestrians. The woman's outfit seamlessly changes into a stylish beige blazer, white blouse, pleated skirt, and high heels while she confidently walks forward carrying a brown leather handbag. Cinematic lighting, ultra-realistic details, smooth transition, Hollywood VFX, volumetric lighting, shallow depth of field, 35mm anamorphic lens, dramatic camera movement, high-end fashion commercial meets superhero transformation, 4K, 16:9

From chaos to confidence, one spark changed everything. Sometimes your greatest power is the courage to transform. Created this 13-second on using Seedance 2.0 on WaveSpeedAI Prompt: A cinematic action sequence featuring a confident young woman with auburn hair tied in a ponytail, wearing a black leather jacket, dark pants, and combat boots. She runs through a ruined city street filled with smoke, debris, abandoned vehicles, and attacking zombie-like creatures. The camera follows with dynamic handheld tracking shots and dramatic slow motion. As enemies surround her, she stops, raises her arms, and a brilliant golden energy erupts from her body, forming glowing rings and powerful light waves that blast the monsters away. The destruction instantly transforms into a luxurious modern shopping boulevard with elegant boutiques, warm golden-hour sunlight, clean streets, and fashionable pedestrians. The woman's outfit seamlessly changes into a stylish beige blazer, white blouse, pleated skirt, and high heels while she confidently walks forward carrying a brown leather handbag. Cinematic lighting, ultra-realistic details, smooth transition, Hollywood VFX, volumetric lighting, shallow depth of field, 35mm anamorphic lens, dramatic camera movement, high-end fashion commercial meets superhero transformation, 4K, 16:9

liana

16,589 просмотров • 2 месяцев назад

Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)

Gemini Omni's motion control is f*cking cracked i just figured out how to turn 1 reference video into 50+ AI videos with the exact same movements... you have a video of someone eating, dancing, using a product, doing whatever complex motion you need. you feed it to Gemini Omni and it recreates that exact motion with a completely new AI character in literally one prompt i've tested this against Kling motion control and it's not even close. Kling falls apart the moment you try anything complex. eating scenes look weird, hand movements get mangled, anything multi-step breaks down completely. Gemini Omni handles all of it if you're still using kling motion control or paying creators to split test your videos, this replaces that entire workflow here's the thing though. you can't just prompt this out of the box. if you try to do motion transfer with default prompting you're going to get errors or the motion won't transfer properly. there's a specific prompting method that makes it work every time so i packaged up the whole system.. here's what you're getting: > full step by step video breakdown > how to find the best reference videos to use > the exact prompting system that allows for motion control transfer so you never get errors > the workflow for batching this out at scale (1 video → 50+) RT + reply "MOTION" and i'll send it over (must follow so i can dm)

Miko

62,149 просмотров • 2 месяцев назад

Blender just became a prompt box. Kimi K3 + Blender MCP can take a simple text prompt and build an entire 3D scene for you. terrain, buildings, lighting, materials, camera movement, animation, even the Python scripts holding everything together. but the crazy part isn’t just that it can build the scene. Kimi can inspect what it created, understand what looks wrong, then go back into Blender and fix the actual scene before rendering again. the camera can be clipping through a tree, the lighting can feel off, the city can look too artificial, and instead of starting over, Kimi can make the changes directly inside Blender. and because everything is happening in Blender, you’re not left with a locked AI-generated video. you get the actual 3D scene. every object, material, light, camera and keyframe stays editable. a few years ago, creating something like this could take weeks of modeling, scripting, lighting and animation. now you can describe the world, let Kimi build the first 90%, and spend your time making the final 10% actually look good. that’s what makes Kimi K3 + Blender MCP so interesting. it’s getting dangerously close to text-to-3D, except the result is a real Blender project you can keep editing.

Blender just became a prompt box. Kimi K3 + Blender MCP can take a simple text prompt and build an entire 3D scene for you. terrain, buildings, lighting, materials, camera movement, animation, even the Python scripts holding everything together. but the crazy part isn’t just that it can build the scene. Kimi can inspect what it created, understand what looks wrong, then go back into Blender and fix the actual scene before rendering again. the camera can be clipping through a tree, the lighting can feel off, the city can look too artificial, and instead of starting over, Kimi can make the changes directly inside Blender. and because everything is happening in Blender, you’re not left with a locked AI-generated video. you get the actual 3D scene. every object, material, light, camera and keyframe stays editable. a few years ago, creating something like this could take weeks of modeling, scripting, lighting and animation. now you can describe the world, let Kimi build the first 90%, and spend your time making the final 10% actually look good. that’s what makes Kimi K3 + Blender MCP so interesting. it’s getting dangerously close to text-to-3D, except the result is a real Blender project you can keep editing.

MIKE

10,434 просмотров • 1 месяц назад

This workflow is perfect for creating short fashion-style cinematic videos. I simplified the original prompts based on willie’s method, and the whole process is now much faster and more stable: 1. Generate a 3×3 keyframe grid (Nano Banana Pro only) Use this simple prompt: “In a 3x3 grid, show this character in different angles, keep the scene the same, random poses. This is far simpler and more efficient than my old prompts. You can generate multiple times and just pick the keyframes you like most. 2. Extract a high-res keyframe (Super stable trick) Take a screenshot of the keyframe you want from the 3×3 grid, send it back to Nano Banana Pro, and simply say: “Give me a high-resolution version.” This method is much more stable than relying on complex upscaling prompts. 3. Generate the video with Kling 2.5 Turbo Upload the first and last frames to Kling 2.5 Turbo and use this prompt: “The camera very slowly and smoothly lowers on a boom.” From my testing, Kling 2.5 Turbo offers the best balance of stability and cost — other models are either less consistent or noticeably more expensive. 4. Final speed adjustment with willie’s tool (Critical step) Use the tool built by willie to fine-tune the playback speed of each clip. This step is essential for getting that premium cinematic feel. I’ll drop the tool link in the comments.

This workflow is perfect for creating short fashion-style cinematic videos. I simplified the original prompts based on willie’s method, and the whole process is now much faster and more stable: 1. Generate a 3×3 keyframe grid (Nano Banana Pro only) Use this simple prompt: “In a 3x3 grid, show this character in different angles, keep the scene the same, random poses. This is far simpler and more efficient than my old prompts. You can generate multiple times and just pick the keyframes you like most. 2. Extract a high-res keyframe (Super stable trick) Take a screenshot of the keyframe you want from the 3×3 grid, send it back to Nano Banana Pro, and simply say: “Give me a high-resolution version.” This method is much more stable than relying on complex upscaling prompts. 3. Generate the video with Kling 2.5 Turbo Upload the first and last frames to Kling 2.5 Turbo and use this prompt: “The camera very slowly and smoothly lowers on a boom.” From my testing, Kling 2.5 Turbo offers the best balance of stability and cost — other models are either less consistent or noticeably more expensive. 4. Final speed adjustment with willie’s tool (Critical step) Use the tool built by willie to fine-tune the playback speed of each clip. This step is essential for getting that premium cinematic feel. I’ll drop the tool link in the comments.

underwood

22,240 просмотров • 9 месяцев назад

Claude Opus 5 x NexLev MCP might be the most unfair combo for building faceless YouTube channels right now So I’m giving away the FULL AI Story channel production system behind it Here’s EVERYTHING that you’ll get inside: → The exact Claude setup that turns Opus 5 into a full faceless YouTube production operator. → NexLev MCP niche validation prompts that find new channels getting 100k+ views without guessing niches manually. → 48-hour velocity check prompt to spot which AI Story angles are actually moving right now. → RPM filtering system so you avoid low-value niches and only build around $12-$20+ RPM opportunities. → Opus 5 JSON script framework for 8,000+ word videos with locked characters, pacing rules, emotional beats, and cliffhangers. → Documentary research brief prompt that verifies dates, names, timelines, and quotes before the script gets written. → ElevenLabs MCP voiceover workflow with narrator matching by niche so the voice fits the audience instead of sounding random. → Higgsfield MCP visual system using Seedance 2.0, Flux 2, and Nano Banana Pro to create animated intros, scene images, and consistent characters. → Thumbnail prompt structure for ChatGPT Image 2.0 so the final video has clean text, high emotion, and a clickable 1280x720 layout. → Full assembly checklist for taking the script, voiceover, animated clips, captions, and thumbnail into an upload-ready video in under 30 minutes. All built from the AI YouTube production playbooks used across: → 120+ Elevate members → $12k/mo average per student → 800M+ total views across the system Like + comment "CLAUDE" and I’ll send you the whole thing (Must be following so I can DM)

Claude Opus 5 x NexLev MCP might be the most unfair combo for building faceless YouTube channels right now So I’m giving away the FULL AI Story channel production system behind it Here’s EVERYTHING that you’ll get inside: → The exact Claude setup that turns Opus 5 into a full faceless YouTube production operator. → NexLev MCP niche validation prompts that find new channels getting 100k+ views without guessing niches manually. → 48-hour velocity check prompt to spot which AI Story angles are actually moving right now. → RPM filtering system so you avoid low-value niches and only build around $12-$20+ RPM opportunities. → Opus 5 JSON script framework for 8,000+ word videos with locked characters, pacing rules, emotional beats, and cliffhangers. → Documentary research brief prompt that verifies dates, names, timelines, and quotes before the script gets written. → ElevenLabs MCP voiceover workflow with narrator matching by niche so the voice fits the audience instead of sounding random. → Higgsfield MCP visual system using Seedance 2.0, Flux 2, and Nano Banana Pro to create animated intros, scene images, and consistent characters. → Thumbnail prompt structure for ChatGPT Image 2.0 so the final video has clean text, high emotion, and a clickable 1280x720 layout. → Full assembly checklist for taking the script, voiceover, animated clips, captions, and thumbnail into an upload-ready video in under 30 minutes. All built from the AI YouTube production playbooks used across: → 120+ Elevate members → $12k/mo average per student → 800M+ total views across the system Like + comment "CLAUDE" and I’ll send you the whole thing (Must be following so I can DM)

gold.

22,429 просмотров • 2 месяцев назад

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.

Shade

537,458 просмотров • 4 месяцев назад

Kling 3.0 is out but Sora 2 is still the GOAT when it comes to AI UGC 🤯 And this custom GPT turns your sh*tty Sora 2 prompts into scroll-stopping UGC 🤯 Tell it your product --> get a timeline-based prompt with shot composition, camera angles, lighting, and timing breakdowns. Copy, paste, generate. Perfect for DTC brands and agencies who are tired of AI video output that looks like garbage. Here's the problem: Most people prompt Sora 2 like "make a UGC video of someone using my skincare product" and wonder why the output is unusable. Sora 2 needs hyper-specific instructions—shot type, lighting, scene details, timing cues. Without that, you get slop. This GPT fixes it: → Input your product (supplement, skincare, SaaS, whatever) → It generates a detailed Sora 2 prompt with full scene breakdown → Includes shot composition, camera movement, and timing → Optimized for 9:16 TikTok/Reels format → Copy directly into Sora 2 and generate No 80,000 word "prompting frameworks", just results. What you get: > Professional UGC prompts in 10 seconds > Consistent output quality every time > Prompts built for vertical video formats > Works for any product type Want free access to the Sora 2 Prompt Generator GPT? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)

Kling 3.0 is out but Sora 2 is still the GOAT when it comes to AI UGC 🤯 And this custom GPT turns your sh*tty Sora 2 prompts into scroll-stopping UGC 🤯 Tell it your product --> get a timeline-based prompt with shot composition, camera angles, lighting, and timing breakdowns. Copy, paste, generate. Perfect for DTC brands and agencies who are tired of AI video output that looks like garbage. Here's the problem: Most people prompt Sora 2 like "make a UGC video of someone using my skincare product" and wonder why the output is unusable. Sora 2 needs hyper-specific instructions—shot type, lighting, scene details, timing cues. Without that, you get slop. This GPT fixes it: → Input your product (supplement, skincare, SaaS, whatever) → It generates a detailed Sora 2 prompt with full scene breakdown → Includes shot composition, camera movement, and timing → Optimized for 9:16 TikTok/Reels format → Copy directly into Sora 2 and generate No 80,000 word "prompting frameworks", just results. What you get: > Professional UGC prompts in 10 seconds > Consistent output quality every time > Prompts built for vertical video formats > Works for any product type Want free access to the Sora 2 Prompt Generator GPT? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)

Mike Futia

22,444 просмотров • 7 месяцев назад

TIMED DIALOGUE IN A NIGHTCLUB. THREE WALLS FALL AT ONCE. Nightclub sketch, cut in two halves. Black-and-white first - a couple making out on a couch, someone laughing off-camera. Then color reveals the setup: guy walks up with a drink, delivers a line, she gives him a one-sentence answer that changes the picture, he pauses, then kisses her anyway. None of them exist. It's fully generated, both halves. - What used to be four problems is now one clip Character consistency across a cut - same two faces in B&W and in color. Two-person dialogue with alternating lip sync - three separate English lines, all on time, all matching mouth shapes. Nightclub lighting - low light, saturated color wash, moving sources - was the last hard lighting environment for AI video to render without collapsing into noise. And a kiss - two faces contacting without merging into each other, which has been one of the persistent tells. Any one of these has been solvable for maybe six months. All four in one sketch was still a demo-reel problem in early 2026. - The B&W cut is doing two jobs The editing choice isn't style. It's engineering. Splitting a 15-second sketch into two 5-7 second clips means the model only has to hold consistency inside each segment, not across the whole thing. Monochrome also hides small differences between the two generations - if the girl's face is 3% off between the halves, B&W flattens the delta. Color grading in the second half does the reverse job. Two seams, both hidden by the aesthetic. - The comic beat is the actual craft Generating a kiss is one problem. Generating a kiss that lands as a punchline is a different one. The half-second where he pauses, processes, and decides not to care - that timing has to be prompted specifically. The default output of every current model is a rushed sequence with no beats. Deadpan comic delivery out of AI video means the operator wrote the prompt the way a screenwriter would - pauses, reactions, holds, all specified frame by frame. - What it costs Two 5-7 second clips at $3-5 each with in-model audio. Locked character references for both actors so the faces match across the cut. Prompt structured as a mini-script with beat notation. Realistically 40-60 rerolls to land the timing on all three spoken lines and the kiss. Under $200 in compute. A weekend from concept to publish-ready. - What this actually opens Short-form comedy has been the one segment of content nobody was making with AI video yet, because you can't fake comic timing when your output has drift and glitches. That barrier just came down. Which means every sketch account, every meme page, every stand-up clip factory now has a pipeline that doesn't require booking actors, renting a location, or getting a laugh out of a live crew. That's a real shift in a market that produces billions of views a month.

TIMED DIALOGUE IN A NIGHTCLUB. THREE WALLS FALL AT ONCE. Nightclub sketch, cut in two halves. Black-and-white first - a couple making out on a couch, someone laughing off-camera. Then color reveals the setup: guy walks up with a drink, delivers a line, she gives him a one-sentence answer that changes the picture, he pauses, then kisses her anyway. None of them exist. It's fully generated, both halves. - What used to be four problems is now one clip Character consistency across a cut - same two faces in B&W and in color. Two-person dialogue with alternating lip sync - three separate English lines, all on time, all matching mouth shapes. Nightclub lighting - low light, saturated color wash, moving sources - was the last hard lighting environment for AI video to render without collapsing into noise. And a kiss - two faces contacting without merging into each other, which has been one of the persistent tells. Any one of these has been solvable for maybe six months. All four in one sketch was still a demo-reel problem in early 2026. - The B&W cut is doing two jobs The editing choice isn't style. It's engineering. Splitting a 15-second sketch into two 5-7 second clips means the model only has to hold consistency inside each segment, not across the whole thing. Monochrome also hides small differences between the two generations - if the girl's face is 3% off between the halves, B&W flattens the delta. Color grading in the second half does the reverse job. Two seams, both hidden by the aesthetic. - The comic beat is the actual craft Generating a kiss is one problem. Generating a kiss that lands as a punchline is a different one. The half-second where he pauses, processes, and decides not to care - that timing has to be prompted specifically. The default output of every current model is a rushed sequence with no beats. Deadpan comic delivery out of AI video means the operator wrote the prompt the way a screenwriter would - pauses, reactions, holds, all specified frame by frame. - What it costs Two 5-7 second clips at $3-5 each with in-model audio. Locked character references for both actors so the faces match across the cut. Prompt structured as a mini-script with beat notation. Realistically 40-60 rerolls to land the timing on all three spoken lines and the kiss. Under $200 in compute. A weekend from concept to publish-ready. - What this actually opens Short-form comedy has been the one segment of content nobody was making with AI video yet, because you can't fake comic timing when your output has drift and glitches. That barrier just came down. Which means every sketch account, every meme page, every stand-up clip factory now has a pipeline that doesn't require booking actors, renting a location, or getting a laugh out of a live crew. That's a real shift in a market that produces billions of views a month.

capONE 💎

82,533 просмотров • 1 месяц назад

I FOUND A BEACH ACCOUNT WITH 900K FOLLOWERS POSTING GOLDEN HOUR BIKINI VIDEOS FROM A TROPICAL ISLAND. I RECOGNIZED THE BUTTERFLY. IT IS THE SAME TATTOO FROM THE 7-ELEVEN GIRL. SAME OPERATOR. DIFFERENT BODY. Blue micro bikini. Butterfly tattoo on the chest. Text tattoo on the ribs. Roses on the arms. She is standing on a tropical beach at golden hour. Sand. Water. Driftwood. Trees behind her. The sun is low and backlights her from the right. Haze in the air. She moves slowly. Adjusts the top. Runs her hands through her hair. Lifts her arms above her head. Twelve seconds. It looks like a photographer's reel from Tulum. I have seen that butterfly before. The 7-Eleven girl. Different face. Different body. Different hair. Same butterfly on the same spot on the chest. Same wings. Same position between the collarbones. Two accounts. Two "girls." One tattoo pulled from the same prompt. The golden hour is not aesthetic. It is camouflage. Backlight creates lens flare. Lens flare softens edges. Soft edges hide the places where AI skin meets AI hair and the render breaks down. Every frame has a warm haze across it that the viewer reads as "cinematic" and the operator uses as a filter to cover artifacts. This is not a girl filmed at sunset. This is a render passed through the sun. The bikini is the smallest amount of fabric the algorithm will not flag. The strings are nearly invisible. The triangles cover the minimum. The operator did not pick blue for aesthetics. Blue metallic fabric catches golden hour light and shifts color between frames. It goes from royal blue to purple to copper depending on the angle. The viewer's eye tracks the color shift the way it tracked the metallic bikini in the mirror clip. The fabric is the distraction. The skin is the content. She never faces the camera straight on. In twelve seconds she is always turned fifteen degrees to the side. Profile shots. Three-quarter shots. One moment where she raises her arms and looks at the camera but her chin is tilted down so her eyes are in shadow. The operator learned what the mirror girl knew and what the car girl knew. Straight angles break AI faces. Every other angle sells them. The text tattoo on her ribs says something in cursive. It is readable in one frame, blurred in the next, and different in the third. The words change mid-video. Real ink does not rewrite itself. Prompts do. But nobody pauses a golden hour beach video to read a rib tattoo. The operator knows the viewer is not looking at the ribs. The driftwood on the sand behind her is the same log in every frame. But the sand around it changes. The waterline shifts forward in one cut and backward in the next. Real tides do not reverse in twelve seconds. The beach is a backdrop that was rendered once and the water was animated on top of it without tracking the continuity. The butterfly connects everything. The 7-Eleven. The beach. Two characters that were never in the same prompt but came from the same operator who never changed the tattoo line. One detail. One mistake. Two accounts exposed. That beach has never had a sunset. That sand has never been wet. But the butterfly is still the same.

framexin

13,921 просмотров • 26 дней назад

Megan Fox really knows how to draw everyone's attention, don't you think?🤩😏 VIdeo by Kling 3.0 via Higgsfield AI (More videos for my subscribers!) Video & Image prompts below👇🏻 🔴 Video Prompt: Subject slowly rises from kneeling position on fluffy white rug — movement begins with a natural, fluid push upward from knees to standing. As she rises, hips sway gently and playfully, body moves naturally with soft feminine energy, not exaggerated. Hair still mid-ponytail, hands finish tying it as she stands. Camera rises simultaneously with her, tracking from chest level up to face level as she reaches full standing height. Full body visible throughout the rise. At 8 seconds: she leans slightly forward toward camera, eyes locked, and softly tongue out to the camera lens. At 9-10 seconds: she pulls back, gives one final direct look into camera, then flashes a peace sign with a slight smirk. Style: Cinematic, smooth camera movement, natural bedroom lighting with soft daylight from window, warm neon glow from "Keor" sign in background. Realistic, no sudden cuts, fluid motion throughout. 10 seconds total. 🔴 Image Prompt: A Megan Fox with fair skin, freckles across her face and shoulders, and striking blue eyes is kneeling on a thick, fluffy white rug in a messy bedroom. She has long, black hair that she is actively gathering with both hands to tie into a high ponytail; her right hand holds the base of the ponytail near the crown of her head while her left hand pulls the length of her hair through. She is wearing a fitted light green lime ribbed tank top with V neckline and fuchsia gym shorts bottoms. Her expression is direct and slightly serious as she looks straight at the camera from a high angle. The bedroom background features an unmade pink bed with rumpled light-colored sheets and pillows, scattered clothes on the floor and bed, a white nightstand with a modern lamp, a stack of books, a big poster of Inuyasha manga characters, a little neon written 'Keor' on wall, and a window with sheer curtains letting in natural daylight. The overall scene has a casual, lived-in atmosphere with soft natural lighting."

Megan Fox really knows how to draw everyone's attention, don't you think?🤩😏 VIdeo by Kling 3.0 via Higgsfield AI (More videos for my subscribers!) Video & Image prompts below👇🏻 🔴 Video Prompt: Subject slowly rises from kneeling position on fluffy white rug — movement begins with a natural, fluid push upward from knees to standing. As she rises, hips sway gently and playfully, body moves naturally with soft feminine energy, not exaggerated. Hair still mid-ponytail, hands finish tying it as she stands. Camera rises simultaneously with her, tracking from chest level up to face level as she reaches full standing height. Full body visible throughout the rise. At 8 seconds: she leans slightly forward toward camera, eyes locked, and softly tongue out to the camera lens. At 9-10 seconds: she pulls back, gives one final direct look into camera, then flashes a peace sign with a slight smirk. Style: Cinematic, smooth camera movement, natural bedroom lighting with soft daylight from window, warm neon glow from "Keor" sign in background. Realistic, no sudden cuts, fluid motion throughout. 10 seconds total. 🔴 Image Prompt: A Megan Fox with fair skin, freckles across her face and shoulders, and striking blue eyes is kneeling on a thick, fluffy white rug in a messy bedroom. She has long, black hair that she is actively gathering with both hands to tie into a high ponytail; her right hand holds the base of the ponytail near the crown of her head while her left hand pulls the length of her hair through. She is wearing a fitted light green lime ribbed tank top with V neckline and fuchsia gym shorts bottoms. Her expression is direct and slightly serious as she looks straight at the camera from a high angle. The bedroom background features an unmade pink bed with rumpled light-colored sheets and pillows, scattered clothes on the floor and bed, a white nightstand with a modern lamp, a stack of books, a big poster of Inuyasha manga characters, a little neon written 'Keor' on wall, and a window with sheer curtains letting in natural daylight. The overall scene has a casual, lived-in atmosphere with soft natural lighting."

KeorUnreal

108,414 просмотров • 5 месяцев назад