Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

How to generate 3D miniature city models and animate them using Kling AI? This visual effect can be created with Kling O1, and then rotated in 3D using Image to Video. The image prompt used for generation is as follows: Present a clear, 45° top-down isometric miniature 3D cartoon... scene of New York featuring its most iconic landmarks and architectural elements. Use soft, refined textures with realistic PBR materials and gentle, lifelike lighting and shadows. Integrate the current weather conditions directly into the city environment to create an immersive atmospheric mood.Use a clean, minimalistic composition with a soft, solid-colored background. At the top-center, place the title "New York" in large bold text in white. You can create different effects by changing the city name according to your needs.show more

Kling AI

133,643 subscribers

34,303 Aufrufe • vor 9 Monaten •via X (Twitter)

Wissenschaft & Technologie

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Have you ever imagined your city turning into a Lego world? With Kling O1, that fantasy becomes real! Tools: Kling IMAGE O1, Kling Image to Video Use this image-generation prompt: Present a clear, 45° top-down isometric miniature Lego-style 3D cartoon scene of New York built entirely from plastic bricks. Recreate the city’s most iconic landmarks and architectural elements using distinctive Lego block geometry. Use clean, glossy brick textures with subtle PBR reflections and soft, playful lighting and shadows. Keep the composition minimalistic with a soft, solid-colored background. Feeling inspired? Just change the city name and watch a whole new Lego universe come to life!

Have you ever imagined your city turning into a Lego world? With Kling O1, that fantasy becomes real! Tools: Kling IMAGE O1, Kling Image to Video Use this image-generation prompt: Present a clear, 45° top-down isometric miniature Lego-style 3D cartoon scene of New York built entirely from plastic bricks. Recreate the city’s most iconic landmarks and architectural elements using distinctive Lego block geometry. Use clean, glossy brick textures with subtle PBR reflections and soft, playful lighting and shadows. Keep the composition minimalistic with a soft, solid-colored background. Feeling inspired? Just change the city name and watch a whole new Lego universe come to life!

Kling AI

38,391 Aufrufe • vor 9 Monaten

Exploring Las Vegas in a whole new way ✨🇺🇸 A miniature world brought to life with iconic landmarks and cinematic details. Made With SuperCool Supercool.com Prompt ⤵️ Create a premium vertical 3D isometric travel diorama poster of LAS VEGAS, USA. Show a highly detailed miniature version of the Las Vegas Strip surrounded by realistic desert terrain and rugged Nevada mountains. Include recognizable Las Vegas landmarks such as the Bellagio resort with illuminated fountains, the Las Vegas Sphere, Paris Las Vegas Eiffel Tower, Luxor pyramid, New York-New York with the Statue of Liberty, Venetian-style resort architecture, High Roller, iconic Las Vegas boulevard, palm trees, glowing signs, roads, and tiny realistic vehicles. Use a high-angle three-quarter isometric perspective with realistic architectural miniatures, cinematic lighting, detailed textures, realistic glass, stone, metal, water, vegetation, roads, and atmospheric depth. The entire miniature terrain should form one cohesive landscape, not separate objects. Keep the Las Vegas Strip as the main visual axis and make the Sphere and Bellagio fountains prominent hero elements. Use a sophisticated editorial travel-poster composition with generous warm off-white negative space around the diorama. The miniature terrain should occupy approximately the lower 70% of the vertical composition. At the top center, display exactly: LAS VEGAS USA · NORTH AMERICA 🇺🇸 Use bold condensed uppercase typography for “LAS VEGAS” and smaller widely spaced typography for “USA · NORTH AMERICA”. Softly feather and dissolve the left, right, and bottom edges of the terrain into subtle atmospheric haze and soft shadows. Do not create a hard rectangular base, pedestal, sticker-like border, or toy display platform. Add subtle brand placement in the bottom-right corner: Keep the brand text small, clean, sharp, elegant, and clearly readable. Do not make it look like a large watermark. Style: ultra-realistic cinematic 3D architectural visualization, premium miniature craftsmanship, realistic aerial photography aesthetic, sophisticated travel editorial poster, detailed ambient occlusion, natural shadows, realistic reflections, cinematic depth, high detail, photorealistic materials. Vertical 3:4 composition. No cartoon style, no low-poly look, no chibi elements, no generic city, no Los Angeles landmarks, no duplicated landmarks, no distorted architecture, no incorrect text, no extra text, no unnecessary borders, no hard terrain edges, no oversized logo, no watermark.

Alina Ai

11,854 Aufrufe • vor 16 Tagen

MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."

MiniMax H3 Instead of sharing the prompts for each of these videos, I thought it would be more useful to share how I created that prompts. All of the videos were generated with text-to-video. First, find an image with the kind of scene, composition and mood you want to recreate. I used a few YouTube playlist thumbnails as references but Pinterest is also a great place to find inspiration. You can even use your own old or nostalgic photographs. Then upload the image to ChatGPT and ask it to describe the scene. The description it gives you can essentially become your text-to-video prompt. From there, you can generate completely new scenes with a similar composition, atmosphere and cinematic language. You can of course use the reference image directly with image-to-video or as a first frame. But if the original image isn't yours, I prefer using it only as visual inspiration and recreating the scene through text-to-video. This is the prompt I use with ChatGPT: "Describe the scene in this image in English, focusing primarily on what is happening, the characters, their actions and body language, the setting and the overall atmosphere. Also briefly describe the composition, framing, camera angle, approximate lens choice, lighting, color palette and cinematic aesthetic. Keep it concise and scene-focused rather than overly technical."

Kōda

55,398 Aufrufe • vor 1 Monat

Create your city inside LovartAI ✨ Just change the names in the prompt and watch Nano Banana 2 make it real. 👀 🚨 Prompt Drop ⤵️ A street-style printed map of [ YOUR CITY ] placed on a clean light wood table, with a photorealistic miniature version of [ YOUR CITY ] emerging from it, featuring [ INSERT THE MAIN TOURIST ATTRACTIONS AND ANY CUSTOM ELEMENTS YOU WANT TO ADD ]. All elements rise organically from the map surface, forming a highly detailed 3D urban diorama. Buildings align precisely with the printed streets. Studio soft lighting, cinematic depth of field, ultra-realistic textures, clean atmosphere, premium travel photography aesthetic.

Create your city inside LovartAI ✨ Just change the names in the prompt and watch Nano Banana 2 make it real. 👀 🚨 Prompt Drop ⤵️ A street-style printed map of [ YOUR CITY ] placed on a clean light wood table, with a photorealistic miniature version of [ YOUR CITY ] emerging from it, featuring [ INSERT THE MAIN TOURIST ATTRACTIONS AND ANY CUSTOM ELEMENTS YOU WANT TO ADD ]. All elements rise organically from the map surface, forming a highly detailed 3D urban diorama. Buildings align precisely with the printed streets. Studio soft lighting, cinematic depth of field, ultra-realistic textures, clean atmosphere, premium travel photography aesthetic.

Marcio Lima 利真マルシオ 💎

30,701 Aufrufe • vor 6 Monaten

here's how you can create videos with full camera control using Krea Agent: - take an input image and ask the agent to create a 3D scene with it. - after the scene is created, ask it to create any camera movement you want. - finally, ask it to render the 3D video with Seedance 2.5. try it now 👇

here's how you can create videos with full camera control using Krea Agent: - take an input image and ask the agent to create a 3D scene with it. - after the scene is created, ask it to create any camera movement you want. - finally, ask it to render the 3D video with Seedance 2.5. try it now 👇

Krea

13,582 Aufrufe • vor 9 Tagen

Do you want to create your cutest squeeze toy? Here is the workflow.... First create Inage with Gpt image 2 Here's the prompt modified according to your reference image and creator persona: Prompt: Ultra-realistic whimsical miniature portrait of a tiny stylized female AI creator inspired by the reference image, featuring fair glowing skin, expressive brown eyes, soft feminine facial features, dark wavy hair tied in a messy low bun with loose strands framing her face, and pearl earrings. She is standing on a polished dark wooden table with her arms at her sides, mouth wide open in a perfect "O" shape, cheeks puffed up to an exaggerated cartoon size, and eyes bulging in surprise. A giant realistic human hand enters from the right side, playfully poking her exposed belly, causing a funny squishy reaction. She wears an oversized black Future Vibes AI hoodie, relaxed beige trousers, and white sneakers. The character has adorable bobblehead proportions with a disproportionately large head and tiny body, soft rubbery Pixar-style physics, photorealistic skin textures, expressive facial animation, and cute miniature details. Background is a clean light grey/off-white horizontal panel wall with soft diffused indoor lighting and subtle shadows beneath the figure. Vertical composition, medium close-up shot, centered framing, whimsical cinematic atmosphere, ultra-detailed 3D render, Pixar meets realism, shallow depth of field, 8K masterpiece. Image Created In Vidu AI For Video Prompt Check Below

Do you want to create your cutest squeeze toy? Here is the workflow.... First create Inage with Gpt image 2 Here's the prompt modified according to your reference image and creator persona: Prompt: Ultra-realistic whimsical miniature portrait of a tiny stylized female AI creator inspired by the reference image, featuring fair glowing skin, expressive brown eyes, soft feminine facial features, dark wavy hair tied in a messy low bun with loose strands framing her face, and pearl earrings. She is standing on a polished dark wooden table with her arms at her sides, mouth wide open in a perfect "O" shape, cheeks puffed up to an exaggerated cartoon size, and eyes bulging in surprise. A giant realistic human hand enters from the right side, playfully poking her exposed belly, causing a funny squishy reaction. She wears an oversized black Future Vibes AI hoodie, relaxed beige trousers, and white sneakers. The character has adorable bobblehead proportions with a disproportionately large head and tiny body, soft rubbery Pixar-style physics, photorealistic skin textures, expressive facial animation, and cute miniature details. Background is a clean light grey/off-white horizontal panel wall with soft diffused indoor lighting and subtle shadows beneath the figure. Vertical composition, medium close-up shot, centered framing, whimsical cinematic atmosphere, ultra-detailed 3D render, Pixar meets realism, shallow depth of field, 8K masterpiece. Image Created In Vidu AI For Video Prompt Check Below

Future Vibes AI - Educator

21,769 Aufrufe • vor 2 Monaten

Here's exactly how to create these 3D Animations in under 5 minutes: 1. Generate character with GPT using this prompt: 9X16 image of a 3D hyper-realistic character of (DESIRED CHARACTER), anthropomorphic with a friendly face and large expressive eyes. Unreal Engine 5 render style. Background is a (DESIRED BACKGROUND) 2. Animate the video with VEO 3 3. Clone the entire entire video's voice with elevenlabs and rewrite the entire audio clip and paste it back in with capcut. Thats it, stop wasting money on 3D artists.

Here's exactly how to create these 3D Animations in under 5 minutes: 1. Generate character with GPT using this prompt: 9X16 image of a 3D hyper-realistic character of (DESIRED CHARACTER), anthropomorphic with a friendly face and large expressive eyes. Unreal Engine 5 render style. Background is a (DESIRED BACKGROUND) 2. Animate the video with VEO 3 3. Clone the entire entire video's voice with elevenlabs and rewrite the entire audio clip and paste it back in with capcut. Thats it, stop wasting money on 3D artists.

mtvmald

66,085 Aufrufe • vor 7 Monaten

Google Earth now lets you create AI images using real-world locations. With the new Nano Banana AI feature, you can pick any place in Google Earth, click “Create Image,” and describe what you want to see. The AI can turn that location into a historical scene, a futuristic city or a fantasy world The feature uses Google Earth’s satellite and 3D map data as the base, so the AI-generated images are built from real locations.

Google Earth now lets you create AI images using real-world locations. With the new Nano Banana AI feature, you can pick any place in Google Earth, click “Create Image,” and describe what you want to see. The AI can turn that location into a historical scene, a futuristic city or a fantasy world The feature uses Google Earth’s satellite and 3D map data as the base, so the AI-generated images are built from real locations.

Pirat_Nation 🔴

24,731 Aufrufe • vor 1 Monat

kling 2.6 motion control is actually insane for AI ads you can swap any person in a video with a different person and it looks completely realistic, and its way better than wan 2.2 this unlocks massive testing potential for ads you can test the same ad concept across multiple different ICPs instantly here's the workflow: >generate your new character with nano banana pro >use kling 2.6 motion control to replace the person in your existing video generate a new voice with >elevenlabs to match the character you can take one winning ad and instantly create 10 variations targeting: -different age groups -different genders -different ethnicities -different aesthetics this is the future of ad testing at scale

kling 2.6 motion control is actually insane for AI ads you can swap any person in a video with a different person and it looks completely realistic, and its way better than wan 2.2 this unlocks massive testing potential for ads you can test the same ad concept across multiple different ICPs instantly here's the workflow: >generate your new character with nano banana pro >use kling 2.6 motion control to replace the person in your existing video generate a new voice with >elevenlabs to match the character you can take one winning ad and instantly create 10 variations targeting: -different age groups -different genders -different ethnicities -different aesthetics this is the future of ad testing at scale

Miko

29,467 Aufrufe • vor 9 Monaten

🚨 AI didn’t decide how this cinematic scene should look. I directed it in 3D first. 2 Characters. A stormy city rooftop. A helicopter overhead. One tense confrontation. Try 3D Director Studio 👉 Instead of asking AI to guess the composition, I used Pippitofficial 's 3D Director Studio to place both characters into the scene, position them across the helipad, and define their visual relationship with the helicopter and skyline. Then I brought the 3D setup into Story Studio Canvas as a visual reference and generated the final video with Seedance 2.5. 🎬 Plan every shot. Validate every idea. Shoot with confidence. #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

🚨 AI didn’t decide how this cinematic scene should look. I directed it in 3D first. 2 Characters. A stormy city rooftop. A helicopter overhead. One tense confrontation. Try 3D Director Studio 👉 Instead of asking AI to guess the composition, I used Pippitofficial 's 3D Director Studio to place both characters into the scene, position them across the helipad, and define their visual relationship with the helicopter and skyline. Then I brought the 3D setup into Story Studio Canvas as a visual reference and generated the final video with Seedance 2.5. 🎬 Plan every shot. Validate every idea. Shoot with confidence. #3DDirectorStudio #PippitAI #Seedance25 #PippitPartner

Anthara Fairooz

46,460 Aufrufe • vor 7 Tagen

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

MrNeRF

17,062 Aufrufe • vor 1 Jahr

Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”

Create a 3D model from a single image, set of images or a text prompt in < 1 minute 😮‍💨 This new AI paper called CAT3D shows us that it’ll keep getting easier to produce 3D models from 2D images — whether it’s a sparser real world 3D scan (a few photos instead of hundreds) or your favorite 2D image generator like Midjourney (just an image). How does this magic work? “This architecture is similar to video diffusion models, but with camera pose embeddings for each image instead of time embeddings. The generated views are passed into a robust 3D reconstruction pipeline to create the 3D representation (Zip-NeRF or 3DGS)”

Bilawal Sidhu

92,898 Aufrufe • vor 2 Jahren

I had an early access to GPT-6 Astra and I can say, everyone in the world now has a 3D designer at their fingertips. I gave it an image of a house and asked to create it in 3D with all the details including toys, appliances and furniture. This a full 3D model reconstruction in Blender with geometry that you can manually tweak and run 60fps as a locally rendered "game" on device.

I had an early access to GPT-6 Astra and I can say, everyone in the world now has a 3D designer at their fingertips. I gave it an image of a house and asked to create it in 3D with all the details including toys, appliances and furniture. This a full 3D model reconstruction in Blender with geometry that you can manually tweak and run 60fps as a locally rendered "game" on device.

Tom Krcha

1,440,185 Aufrufe • vor 21 Tagen

A quick test of using 3d drawing with 6DOF controllers as an "instructor" for a generative AI process. There's so many powerful and fun new ways to create just around the corner.. Here I'm using Dreams, Krea and 3daistudio. The 3d model at the end of the video was generated from the Dreams+Krea output in just around 15 seconds. Only the model on the left is a "true" 3d model. #ai #madeindreams

A quick test of using 3d drawing with 6DOF controllers as an "instructor" for a generative AI process. There's so many powerful and fun new ways to create just around the corner.. Here I'm using Dreams, Krea and 3daistudio. The 3d model at the end of the video was generated from the Dreams+Krea output in just around 15 seconds. Only the model on the left is a "true" 3d model. #ai #madeindreams

Martin Nebelong

132,897 Aufrufe • vor 2 Jahren

If you're using match cuts in your video, the invideo editor + agent gives you the fastest, most seamless experience to do it. Here's what we did to create this in ~40 minutes: ~Picked an image with a circle in the centre. Used that image as a reference to generate other images with circular subjects (but you could always just use your own footage). ~Uploaded the audio track I wanted to use into the invideo timeline, and asked the agent to detect all beats and place markers there. ~Asked the agent to then place all my generated images against those markers. ~Used rulers in the editor to maintain clip/subject alignment.

If you're using match cuts in your video, the invideo editor + agent gives you the fastest, most seamless experience to do it. Here's what we did to create this in ~40 minutes: ~Picked an image with a circle in the centre. Used that image as a reference to generate other images with circular subjects (but you could always just use your own footage). ~Uploaded the audio track I wanted to use into the invideo timeline, and asked the agent to detect all beats and place markers there. ~Asked the agent to then place all my generated images against those markers. ~Used rulers in the editor to maintain clip/subject alignment.

Invideo

11,077 Aufrufe • vor 17 Tagen

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 Aufrufe • vor 1 Jahr

ChatGPT-Image-2.0 × HappyHorse 1.0 作り方： Step 1：ChatGPT-Image-2.0で画面を生成 Prompt Step-by-step shampoo routine: first create enough foam in your hands; do not apply shampoo directly to the scalp; apply the foam to the scalp and gently cleanse. Rinse thoroughly with water, then dry at the end. Clean bathroom background, soft lighting, instructional style, photorealistic, high detail Step 2：HappyHorse 1.0で動画化 Prompt Share 124 Following steps in the reference image, a step-by-step shampoo routine: first create enough foam in your hands; do not apply shampoo directly to the scalp; apply the foam to the scalp and gently cleanse. Rinse thoroughly with water, then dry at the end. Clean bathroom background, soft lighting, instructional style, photorealistic, high detail

ChatGPT-Image-2.0 × HappyHorse 1.0 作り方： Step 1：ChatGPT-Image-2.0で画面を生成 Prompt Step-by-step shampoo routine: first create enough foam in your hands; do not apply shampoo directly to the scalp; apply the foam to the scalp and gently cleanse. Rinse thoroughly with water, then dry at the end. Clean bathroom background, soft lighting, instructional style, photorealistic, high detail Step 2：HappyHorse 1.0で動画化 Prompt Share 124 Following steps in the reference image, a step-by-step shampoo routine: first create enough foam in your hands; do not apply shampoo directly to the scalp; apply the foam to the scalp and gently cleanse. Rinse thoroughly with water, then dry at the end. Clean bathroom background, soft lighting, instructional style, photorealistic, high detail

KANA｜東京AI映像

22,601 Aufrufe • vor 5 Monaten

What used to take lights, props, and a full set can now be done inside an agent. This was created using a Glif agent with Kling 2.6 Motion Control and a Nano Banana reference image to lock the look. No physical setup needed. Glif has agents for different creative use cases, so you can pick what fits your workflow and move fast. Check the agents used for this below 👇

What used to take lights, props, and a full set can now be done inside an agent. This was created using a Glif agent with Kling 2.6 Motion Control and a Nano Banana reference image to lock the look. No physical setup needed. Glif has agents for different creative use cases, so you can pick what fits your workflow and move fast. Check the agents used for this below 👇

GLIF

12,858 Aufrufe • vor 8 Monaten

Created this by using a movement sheet as a reference image to animate the dance using Seedance 2.0 + ChatGPT image 2.0 on Yapper GPT Image 2.0 Prompt: Dance Sequence Instruction Sheet [VISUAL STYLE] A composition featuring a highly detailed 3D-rendered female dancer. Designed like a professional choreography guide with a technical, diagram-inspired layout. Clean white background, soft studio lighting, and strong contrast to highlight body movement and posture. [GRID LAYOUT] Structured 4×4 panel grid (16 frames total), evenly spaced with thin black divider lines. Each panel is identical in size and clearly numbered from 1 to 16 to show a continuous dance progression. [CHARACTER] Use image1 as the base character. The same female dancer appears consistently across all panels with accurate likeness and proportions. [WARDROBE] The dancer wears a stylish, performance-ready outfit: a well-fitted top paired with a short, flowy skirt. The look should feel modern and visually appealing while still practical for dance movement. Fabric should subtly respond to motion (slight flow and folds), even in grayscale. [PANEL STRUCTURE – EACH FRAME] Top-left: Step number + short dance move title (e.g., “Step 5 – Spin Transition”) Center: Full-body pose capturing a precise moment in the choreography Bottom-left: 3–4 lines of concise instruction describing the move Overlay: Motion arrows and directional guides illustrating how the dancer transitions [MOTION INDICATORS] Incorporate curved arrows for fluid motion, straight arrows for directional steps, and circular indicators for spins or turns. Emphasize rhythm, weight shifts, and body isolation. [RENDER QUALITY] High-detail sculpted 3D style with smooth grayscale shading, subtle shadows, and clean linework. Maintain a polished, concept-art level finish with clarity in every pose. [RESTRICTIONS] No color, no background scenery, no extra characters, no visual clutter, only the dancer and instructional elements.

Created this by using a movement sheet as a reference image to animate the dance using Seedance 2.0 + ChatGPT image 2.0 on Yapper GPT Image 2.0 Prompt: Dance Sequence Instruction Sheet [VISUAL STYLE] A composition featuring a highly detailed 3D-rendered female dancer. Designed like a professional choreography guide with a technical, diagram-inspired layout. Clean white background, soft studio lighting, and strong contrast to highlight body movement and posture. [GRID LAYOUT] Structured 4×4 panel grid (16 frames total), evenly spaced with thin black divider lines. Each panel is identical in size and clearly numbered from 1 to 16 to show a continuous dance progression. [CHARACTER] Use image1 as the base character. The same female dancer appears consistently across all panels with accurate likeness and proportions. [WARDROBE] The dancer wears a stylish, performance-ready outfit: a well-fitted top paired with a short, flowy skirt. The look should feel modern and visually appealing while still practical for dance movement. Fabric should subtly respond to motion (slight flow and folds), even in grayscale. [PANEL STRUCTURE – EACH FRAME] Top-left: Step number + short dance move title (e.g., “Step 5 – Spin Transition”) Center: Full-body pose capturing a precise moment in the choreography Bottom-left: 3–4 lines of concise instruction describing the move Overlay: Motion arrows and directional guides illustrating how the dancer transitions [MOTION INDICATORS] Incorporate curved arrows for fluid motion, straight arrows for directional steps, and circular indicators for spins or turns. Emphasize rhythm, weight shifts, and body isolation. [RENDER QUALITY] High-detail sculpted 3D style with smooth grayscale shading, subtle shadows, and clean linework. Maintain a polished, concept-art level finish with clarity in every pose. [RESTRICTIONS] No color, no background scenery, no extra characters, no visual clutter, only the dancer and instructional elements.

Johnn

55,075 Aufrufe • vor 5 Monaten

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.

We are getting absurdly close to the point where “learning Blender” means learning how to direct an AI. A shot like this looks soft and playful on the surface, but under it is the usual 3D pain: modeling, layout, materials, lighting, atmosphere, animation, and endless tiny fixes until the frame stops looking dead. That is why Kimi K3 matters. With Blender MCP, you can describe a scene like a robotic goat walking through a dreamy field and let the model help build the environment, place the camera, shape the materials, script the motion, and iterate inside the real Blender project. The real shift is not text-to-image. It is text-to-workflow. Kimi K3 does not just give you a pretty output and disappear. It can help move the actual scene from rough setup to something that looks art-directed. Soon the hardest part of 3D will not be the software. It will be whether your imagination is good enough to deserve tools like this.

Rina

45,651 Aufrufe • vor 1 Monat