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Grok 4.5 just turned Blender into a conversation. Instead of manually importing assets, fixing rigs, placing armies, adjusting cameras, and debugging objects that face the wrong direction, the user simply tells Grok what to change. It builds a medieval battlefield from existing 3D assets, adds horses, knights, a castle,...

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Rina

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1,083,461 просмотров • 26 дней назад •via X (Twitter)

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Blender tutorials are about to age very badly. In this demo, Kimi K3 gets two character references and a plain-English request. Minutes later, it builds the first version inside Blender. The user asks for a better horn, Kimi edits the same scene, and the character starts moving. This is not another AI tool generating one lucky image. It is working with an actual editable 3D project, then changing the geometry after feedback instead of restarting from zero. It still needs human direction, because the first result is rarely the good one. But blockouts, repetitive edits, Blender Python, and basic iteration are becoming a conversation rather than hours of clicking through menus. Kimi K3 + Blender MCP is basically turning 3D modeling into: describe it, inspect it, complain about it, and let the AI fix it.
1:10

Blender tutorials are about to age very badly. In this demo, Kimi K3 gets two character references and a plain-English request. Minutes later, it builds the first version inside Blender. The user asks for a better horn, Kimi edits the same scene, and the character starts moving. This is not another AI tool generating one lucky image. It is working with an actual editable 3D project, then changing the geometry after feedback instead of restarting from zero. It still needs human direction, because the first result is rarely the good one. But blockouts, repetitive edits, Blender Python, and basic iteration are becoming a conversation rather than hours of clicking through menus. Kimi K3 + Blender MCP is basically turning 3D modeling into: describe it, inspect it, complain about it, and let the AI fix it.

Rina

19,092 просмотров • 1 месяц назад

This is much worse for 3D artists than another AI image generator. Claude takes one architectural reference, connects directly to Blender, and starts rebuilding it as an actual editable scene. It blocks out the buildings, matches the camera, adds materials, vegetation, lighting, and then renders the result. The interesting part is not the first generation. Claude inspects its own render, notices the composition and lighting are wrong, then goes back into Blender and keeps changing the scene. The final result is still rough. The materials look basic, the geometry needs cleanup, and an experienced artist would make it far better. But AI is no longer producing a flat image and pretending it understands 3D. It is operating the software, judging its own work, and iterating inside the real project. The “AI cannot do their actual work” argument is getting harder to defend.
1:43

This is much worse for 3D artists than another AI image generator. Claude takes one architectural reference, connects directly to Blender, and starts rebuilding it as an actual editable scene. It blocks out the buildings, matches the camera, adds materials, vegetation, lighting, and then renders the result. The interesting part is not the first generation. Claude inspects its own render, notices the composition and lighting are wrong, then goes back into Blender and keeps changing the scene. The final result is still rough. The materials look basic, the geometry needs cleanup, and an experienced artist would make it far better. But AI is no longer producing a flat image and pretending it understands 3D. It is operating the software, judging its own work, and iterating inside the real project. The “AI cannot do their actual work” argument is getting harder to defend.

Rina

34,431 просмотров • 26 дней назад

Grok Imagine’s image editing literally blows my mind I just edited this entire image from scratch using Grok Imagine and its full set of editing tools The segment + precise editing tools are insanely useful Instead of regenerating the entire image, you can target exactly what you want to change while keeping everything else intact That level of control makes editing much faster and more practical Grok Imagine is becoming a seriously powerful image editor….not just an image generator
1:23

Grok Imagine’s image editing literally blows my mind I just edited this entire image from scratch using Grok Imagine and its full set of editing tools The segment + precise editing tools are insanely useful Instead of regenerating the entire image, you can target exactly what you want to change while keeping everything else intact That level of control makes editing much faster and more practical Grok Imagine is becoming a seriously powerful image editor….not just an image generator

X Freeze

4,142,797 просмотров • 22 дней назад

🇪🇺🇷🇺 "EU started as a peace project and has now become a warmongering monster project" — Belgian MP Tom Vandendriessche “What if the frozen Russian assets are not enough? This is cynicism and belligerence on the part of the EU" He adds that the confiscation of Russian assets is the largest robbery in history "If you want to support Ukraine, do it at your own expense"
1:43

🇪🇺🇷🇺 "EU started as a peace project and has now become a warmongering monster project" — Belgian MP Tom Vandendriessche “What if the frozen Russian assets are not enough? This is cynicism and belligerence on the part of the EU" He adds that the confiscation of Russian assets is the largest robbery in history "If you want to support Ukraine, do it at your own expense"

Lord Bebo

66,085 просмотров • 8 месяцев назад

This BlenderFusion paper basically says "screw trying to describe 3D edits through text" and just... use Blender :-) The idea is pretty straightforward -- instead of trying to cram 3D understanding into a diffusion model, use depth estimation & segmentation to project 2D images into 2.5D meshes, edit them in actual 3D software, then use a fine-tuned diffusion model to make the results photorealistic again. The clever bit is their "dual-stream architecture" -- the model sees both the original scene AND the edited Blender render in parallel, learning to preserve what matters while fixing the inevitable artifacts from transforming imperfect 2.5D/3D reconstructions. They train it with smart masking strategies so it learns when to ignore the original scene (for removals/replacements) and can manipulate objects independently of camera motion. What you get is pretty impressive control -- not just moving objects around, but changing materials, deforming shapes, swapping backgrounds, all while maintaining visual coherence. Neural Assets (one of my favorite papers last year) tried to crack this with learned object tokens, but it struggled with overlapping objects and loses fine details (due to low res DINO encodings). BlenderFusion just sidesteps the whole problem -- want to rotate something 173.5 degrees? Just rotate it in Blender. Want to duplicate an object 8 times? Copy paste away. The diffusion model's only job is making it look photorealistic, not figuring out the 3D underpinnings. The catch? Lacks temporal consistency for animation. Each viewpoint is generated independently, so while a single edit looks great, smoothly animating a car or camera down the street won't work -- you'd get flickering and inconsistencies between frames. That said, this approach is so much more intuitive for finer grain image editing than trying to describe your changes in text prompts. It's the kind of thing that makes you wonder why we're trying to do everything inside neural networks when perfectly good 3D tools already exist -- giving you the best of both worlds.
0:21

This BlenderFusion paper basically says "screw trying to describe 3D edits through text" and just... use Blender :-) The idea is pretty straightforward -- instead of trying to cram 3D understanding into a diffusion model, use depth estimation & segmentation to project 2D images into 2.5D meshes, edit them in actual 3D software, then use a fine-tuned diffusion model to make the results photorealistic again. The clever bit is their "dual-stream architecture" -- the model sees both the original scene AND the edited Blender render in parallel, learning to preserve what matters while fixing the inevitable artifacts from transforming imperfect 2.5D/3D reconstructions. They train it with smart masking strategies so it learns when to ignore the original scene (for removals/replacements) and can manipulate objects independently of camera motion. What you get is pretty impressive control -- not just moving objects around, but changing materials, deforming shapes, swapping backgrounds, all while maintaining visual coherence. Neural Assets (one of my favorite papers last year) tried to crack this with learned object tokens, but it struggled with overlapping objects and loses fine details (due to low res DINO encodings). BlenderFusion just sidesteps the whole problem -- want to rotate something 173.5 degrees? Just rotate it in Blender. Want to duplicate an object 8 times? Copy paste away. The diffusion model's only job is making it look photorealistic, not figuring out the 3D underpinnings. The catch? Lacks temporal consistency for animation. Each viewpoint is generated independently, so while a single edit looks great, smoothly animating a car or camera down the street won't work -- you'd get flickering and inconsistencies between frames. That said, this approach is so much more intuitive for finer grain image editing than trying to describe your changes in text prompts. It's the kind of thing that makes you wonder why we're trying to do everything inside neural networks when perfectly good 3D tools already exist -- giving you the best of both worlds.

Bilawal Sidhu

34,440 просмотров • 1 год назад

"...the institutions aren't functioning. And you can tell because instead of giving you noisy, good advice, instead of giving you random advice, they give you upside down pyramids. They give you the inverse of the right advice. And that is an alarming fact. It tells you that there is a process in there that knows what to do and tells you the opposite." - Bret Weinstein
0:35

"...the institutions aren't functioning. And you can tell because instead of giving you noisy, good advice, instead of giving you random advice, they give you upside down pyramids. They give you the inverse of the right advice. And that is an alarming fact. It tells you that there is a process in there that knows what to do and tells you the opposite." - Bret Weinstein

The DarkHorse Podcast

29,107 просмотров • 7 месяцев назад

Meta released SAM3 a model to reconstruct objects from segmented images. I tried it and here are 4 things I found out - The generation is incredibly fast when compared with other 3D GenAI models - The models generated are not meshes but Gaussian Splatting. I was surprised that in the paper it was not mentioned anywhere - When generating multiple objects the arrangement is a bit messed up. - Unfortunately, it is impossible to download any of the .ply generated 😔 I cannot stop thinking about the implications of this in XR. Objects in the real world could be almost instantly turned into photorealistic copies and even rearranged to the user's wish.
0:43

Meta released SAM3 a model to reconstruct objects from segmented images. I tried it and here are 4 things I found out - The generation is incredibly fast when compared with other 3D GenAI models - The models generated are not meshes but Gaussian Splatting. I was surprised that in the paper it was not mentioned anywhere - When generating multiple objects the arrangement is a bit messed up. - Unfortunately, it is impossible to download any of the .ply generated 😔 I cannot stop thinking about the implications of this in XR. Objects in the real world could be almost instantly turned into photorealistic copies and even rearranged to the user's wish.

Gabriele Romagnoli

32,441 просмотров • 9 месяцев назад

Took Grok 4.6 out for a spin this weekend and built this horde shooter prototype on ThreeJS. It is a big step up from Grok 4.5! I've always wanted to try building a horde shooter game with thousands of enemies onscreen and used Grok 4.6 for pretty much everything (including UI, vfx, and even the sfx!), except the 3D models for the characters, which were made and rigged with Meshy. I'm also amazed that ThreeJS with WebGPU can really handle thousands of enemies onscreen at the same time, though I think I pushed the limit here with Depth of Field and some other fancy shaders. Very fun!
0:48

Took Grok 4.6 out for a spin this weekend and built this horde shooter prototype on ThreeJS. It is a big step up from Grok 4.5! I've always wanted to try building a horde shooter game with thousands of enemies onscreen and used Grok 4.6 for pretty much everything (including UI, vfx, and even the sfx!), except the 3D models for the characters, which were made and rigged with Meshy. I'm also amazed that ThreeJS with WebGPU can really handle thousands of enemies onscreen at the same time, though I think I pushed the limit here with Depth of Field and some other fancy shaders. Very fun!

Danny Limanseta

63,198 просмотров • 14 дней назад

Cover evolution for Maintenance: Of Everything, Part One. “The cover of the book honors the idea of Kintsugi, the Japanese art of basically repairing broken pottery with a kind of a gold glue. And so it not only fixes it, but it makes it more beautiful. And you honor the mistake that broke it, and you honor the repair . . . So Kintsugi is a way of kind of just honoring the fact that things do break. But nobody actually wants things to break.” —Stewart Brand Design Pablo Delcan, Art direction 🎨
0:46

Cover evolution for Maintenance: Of Everything, Part One. “The cover of the book honors the idea of Kintsugi, the Japanese art of basically repairing broken pottery with a kind of a gold glue. And so it not only fixes it, but it makes it more beautiful. And you honor the mistake that broke it, and you honor the repair . . . So Kintsugi is a way of kind of just honoring the fact that things do break. But nobody actually wants things to break.” —Stewart Brand Design Pablo Delcan, Art direction 🎨

🎨

216,065 просмотров • 7 месяцев назад

The interesting part about GPT in CAD is not that it can make a shape. It is that through MCP, GPT can work directly inside Onshape and turn a plain-English description into actual CAD geometry you can keep editing. In this case, the model builds a radial mechanical part with a central hub, repeated ribs, an outer ring, and a full pattern of mounting holes, all inside the real Onshape document. There is no fake “3D-looking” render hiding the hard part. The geometry exists in the CAD workspace, can be rotated, inspected, modified, and used as the starting point for the next iteration. That changes the workflow from manually clicking through every feature to describing the structure, letting GPT build the first pass, then correcting what matters. Once MCP gets reliable enough, the boring part of CAD starts looking less like modeling and more like supervising.
0:21

The interesting part about GPT in CAD is not that it can make a shape. It is that through MCP, GPT can work directly inside Onshape and turn a plain-English description into actual CAD geometry you can keep editing. In this case, the model builds a radial mechanical part with a central hub, repeated ribs, an outer ring, and a full pattern of mounting holes, all inside the real Onshape document. There is no fake “3D-looking” render hiding the hard part. The geometry exists in the CAD workspace, can be rotated, inspected, modified, and used as the starting point for the next iteration. That changes the workflow from manually clicking through every feature to describing the structure, letting GPT build the first pass, then correcting what matters. Once MCP gets reliable enough, the boring part of CAD starts looking less like modeling and more like supervising.

Rina

16,772 просмотров • 14 дней назад

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.
0:42

Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields paper page: Editing a local region or a specific object in a 3D scene represented by a NeRF is challenging, mainly due to the implicit nature of the scene representation. Consistently blending a new realistic object into the scene adds an additional level of difficulty. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest in an existing NeRF scene, based on text prompts or image patches, along with a 3D ROI box. Our method leverages a pretrained language-image model to steer the synthesis towards a user-provided text prompt or image patch, along with a 3D MLP model initialized on an existing NeRF scene to generate the object and blend it into a specified region in the original scene. We allow local editing by localizing a 3D ROI box in the input scene, and seamlessly blend the content synthesized inside the ROI with the existing scene using a novel volumetric blending technique. To obtain natural looking and view-consistent results, we leverage existing and new geometric priors and 3D augmentations for improving the visual fidelity of the final result. We test our framework both qualitatively and quantitatively on a variety of real 3D scenes and text prompts, demonstrating realistic multi-view consistent results with much flexibility and diversity compared to the baselines. Finally, we show the applicability of our framework for several 3D editing applications, including adding new objects to a scene, removing/replacing/altering existing objects, and texture conversion.

AK

62,768 просмотров • 3 лет назад

AI in CAD just went from “make me a part” to “build me the damn machine.” Using Autodesk Fusion MCP, the agent is working inside a real mechanical assembly with a chassis, shafts, gears, bearings, brackets, repeated hardware, and multiple components that all have to stay aligned with each other. That is a completely different problem from generating one clean 3D object. You can inspect the assembly from different angles, isolate sections, dig into individual joints, and keep working on the same engineering project instead of starting from another prompt-generated blob. The important part is not that AI can create geometry anymore. It is that it can operate inside the structure of a real CAD project where every component depends on what is around it. Once this gets reliable, a lot of mechanical CAD starts looking suspiciously simple: describe the system, inspect what the agent built, then tell it what to change.
0:29

AI in CAD just went from “make me a part” to “build me the damn machine.” Using Autodesk Fusion MCP, the agent is working inside a real mechanical assembly with a chassis, shafts, gears, bearings, brackets, repeated hardware, and multiple components that all have to stay aligned with each other. That is a completely different problem from generating one clean 3D object. You can inspect the assembly from different angles, isolate sections, dig into individual joints, and keep working on the same engineering project instead of starting from another prompt-generated blob. The important part is not that AI can create geometry anymore. It is that it can operate inside the structure of a real CAD project where every component depends on what is around it. Once this gets reliable, a lot of mechanical CAD starts looking suspiciously simple: describe the system, inspect what the agent built, then tell it what to change.

Rina

14,154 просмотров • 11 дней назад

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:
1:07

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,524 просмотров • 8 месяцев назад

🚨 ELON MUSK ON GROK AND MEDICINE: “You take a photograph of your blood work, upload it from your phone to Grok. It will understand what all the data results are and tell you if there’s something wrong. I haven’t seen it be wrong yet. I think AI could actually be very helpful with medical stuff. AI can look at all the studies, look at all the data, cross-check everything, and give you good recommendations. Even as it is right now, you can upload your X-rays and MRI images to Grok, and it will give you a medical diagnosis. That diagnosis, from what I’ve seen, is at least as good. I’ve certainly seen cases where it’s actually better than what the doctor told you.”
0:56

🚨 ELON MUSK ON GROK AND MEDICINE: “You take a photograph of your blood work, upload it from your phone to Grok. It will understand what all the data results are and tell you if there’s something wrong. I haven’t seen it be wrong yet. I think AI could actually be very helpful with medical stuff. AI can look at all the studies, look at all the data, cross-check everything, and give you good recommendations. Even as it is right now, you can upload your X-rays and MRI images to Grok, and it will give you a medical diagnosis. That diagnosis, from what I’ve seen, is at least as good. I’ve certainly seen cases where it’s actually better than what the doctor told you.”

America News

52,775 просмотров • 1 день назад

WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓
0:20

WTF, GROK BOT JUST MADE AI AGENTS AVAILABLE TO LITERALLY ANYONE – CREATING CONTENT HAS NEVER BEEN THIS EASY, EVEN IF YOU'VE NEVER MADE ANYTHING BEFORE Content was never a talent problem. It's a headcount problem. One person doing research, design, copy, analytics, timing and publishing – that's six jobs. The switching between them is what kills consistency, not a lack of ideas. Here's what one of these setups actually looks like. A Chief of Staff sits in the middle and routes every task. Nothing lands on the human. → Researcher tracks what's actually moving and pulls real sources instead of guesswork → Writer turns that research into finished copy, ready to review → Visualiser gets fed a few reference visuals once, then ships everything in that style → Analyst reads the numbers and tells the rest of the team what worked → Scheduler owns timing and holds the queue → Publisher ships it The part that makes it work: every agent on Grok Bot gets its own persistent computer, browser and file system – and they all share memory. So the research is already sitting inside the draft before the draft starts. No copy-pasting between tools. No approving every step. No human in the middle. You can even teach an agent a repetitive task by recording yourself doing it once. Start recording, do the thing, stop. It learns the pattern. And that's the real shift. Nobody needs AI to tell them what to post. They need it to delete the 40 steps between the idea and the post. Everyone has a backlog of things they've meant to make for months. This is what starts clearing it. Full breakdown of the setup in the article below ↓

SCOTTY BEAM

4,796,074 просмотров • 11 дней назад

I'm not a 3d modeler. I barely know how to open blender let alone do anything functional in it. But with the siddharth ahuja blender MCP and Claude I was able to vibe code an entire environment for my upcoming grow a garden game in Portals Claude used the MCP to assemble and arrange every single asset in this space (i literally don't even know how to import an asset into blender lol) within the spawn of a few hours and with access to a folder of low poly assets. When I had it start doing its own modeling, it even matched the style of the environment to keep things consistent If you are building UGC games and you don't know anything about modeling or design like me, you gotta try it out...
0:45

I'm not a 3d modeler. I barely know how to open blender let alone do anything functional in it. But with the siddharth ahuja blender MCP and Claude I was able to vibe code an entire environment for my upcoming grow a garden game in Portals Claude used the MCP to assemble and arrange every single asset in this space (i literally don't even know how to import an asset into blender lol) within the spawn of a few hours and with access to a folder of low poly assets. When I had it start doing its own modeling, it even matched the style of the environment to keep things consistent If you are building UGC games and you don't know anything about modeling or design like me, you gotta try it out...

Hugh MFER (PORTALS Guy)

180,631 просмотров • 7 месяцев назад

It took me less than 10 minutes to create this scene. The longest part was finding the style for my initial image. I used Grok Imagine's extension video to ensure perfect continuity of style, voice, and music. Then I animated it, prompting in my native language without asking for any dialogue. Grok understood everything I asked and created a dialogue between the children in perfect French. I later posted the same children with a prompt in English, and this time, the dialogue was in English. I used Grok Imagine to create the image, animate it, and then extend the video to 30 seconds. I then started again with an image that I modified through editing and used it to tell another story.
0:50

It took me less than 10 minutes to create this scene. The longest part was finding the style for my initial image. I used Grok Imagine's extension video to ensure perfect continuity of style, voice, and music. Then I animated it, prompting in my native language without asking for any dialogue. Grok understood everything I asked and created a dialogue between the children in perfect French. I later posted the same children with a prompt in English, and this time, the dialogue was in English. I used Grok Imagine to create the image, animate it, and then extend the video to 30 seconds. I then started again with an image that I modified through editing and used it to tell another story.

Déborah

501,323 просмотров • 6 месяцев назад

🇺🇦 Zelensky: At the heart of Russia’s war against Ukraine is the false claim that Ukraine does not exist, that it is supposedly part of Russia. It is not just a matter of history, not just politics or ideology. This is the basis of their goal – to completely wipe Ukraine off the face of the earth. And not just Ukraine. Russia has repeatedly openly stated that it wants to control all its neighbors. It wants to decide what security should be in Europe and whether it should exist at all. Russia has spread its idea of ​​war all the way to Syria and Africa. This is truly a global threat. It is important that many in the world perceive it as such – as a global threat.
0:55

🇺🇦 Zelensky: At the heart of Russia’s war against Ukraine is the false claim that Ukraine does not exist, that it is supposedly part of Russia. It is not just a matter of history, not just politics or ideology. This is the basis of their goal – to completely wipe Ukraine off the face of the earth. And not just Ukraine. Russia has repeatedly openly stated that it wants to control all its neighbors. It wants to decide what security should be in Europe and whether it should exist at all. Russia has spread its idea of ​​war all the way to Syria and Africa. This is truly a global threat. It is important that many in the world perceive it as such – as a global threat.

MAKS 25 🇺🇦👀

13,932 просмотров • 4 месяцев назад