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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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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.
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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.

Rina

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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.
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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 Aufrufe • vor 10 Tagen

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
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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

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🇪🇺🇷🇺 "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"
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🇪🇺🇷🇺 "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 Aufrufe • vor 8 Monaten

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.
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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

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"...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
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"...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

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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

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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 🎨
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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 🎨

🎨

215,674 Aufrufe • vor 6 Monaten

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

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🚀 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:
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🚀 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

176,105 Aufrufe • vor 8 Monaten

WHAT IF GRAVITY ISN’T A FORCE - BUT THE SHADOW OF AN EXTRA DIMENSION? Point → line → square → cube. 0D → 1D → 2D → 3D. Now connect 2 cubes. Wait. What the hell do you even get? That’s the 4th spatial dimension - and no, your brain can’t picture it. But your body feels it every second. Mass doesn’t pull on space. It bends it. Curves it into a direction you can’t see - but live inside. That strange invisible push? That’s gravity. Not down. Not up. Sideways through the universe. Welcome to the part of reality you were never supposed to notice. (And don’t even ask about the 5th. That’s where timelines blend.) Source: scribblemouth_
1:48

WHAT IF GRAVITY ISN’T A FORCE - BUT THE SHADOW OF AN EXTRA DIMENSION? Point → line → square → cube. 0D → 1D → 2D → 3D. Now connect 2 cubes. Wait. What the hell do you even get? That’s the 4th spatial dimension - and no, your brain can’t picture it. But your body feels it every second. Mass doesn’t pull on space. It bends it. Curves it into a direction you can’t see - but live inside. That strange invisible push? That’s gravity. Not down. Not up. Sideways through the universe. Welcome to the part of reality you were never supposed to notice. (And don’t even ask about the 5th. That’s where timelines blend.) Source: scribblemouth_

Mario Nawfal

174,614 Aufrufe • vor 1 Jahr

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.
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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 Aufrufe • vor 5 Monaten

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...
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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)

180,520 Aufrufe • vor 6 Monaten

GROK JUST TURNED VOICE AI INTO A REAL PRODUCT, FAST, AND EVERYWHERE xAI just opened Grok Voice to developers, and this isn’t some early experiment dressed up as a launch. It’s the same system already running inside millions of Teslas, now exposed through an API that actually works in the real world. Speed is the first thing that jumps out. Grok responds in under a second, which means conversations flow instead of stalling. That alone separates it from most voice AI people tolerate rather than enjoy. Language is the next flex. Grok handles dozens of them, switches mid-sentence, and keeps accents and tone intact. It sounds like someone talking, not software guessing. Then there’s capability. Grok can search live data, pull from X, use tools, plan routes, and control systems. In Teslas, it already acts like a co-pilot, not a novelty. No token gymnastics. Source: xAI
0:25

GROK JUST TURNED VOICE AI INTO A REAL PRODUCT, FAST, AND EVERYWHERE xAI just opened Grok Voice to developers, and this isn’t some early experiment dressed up as a launch. It’s the same system already running inside millions of Teslas, now exposed through an API that actually works in the real world. Speed is the first thing that jumps out. Grok responds in under a second, which means conversations flow instead of stalling. That alone separates it from most voice AI people tolerate rather than enjoy. Language is the next flex. Grok handles dozens of them, switches mid-sentence, and keeps accents and tone intact. It sounds like someone talking, not software guessing. Then there’s capability. Grok can search live data, pull from X, use tools, plan routes, and control systems. In Teslas, it already acts like a co-pilot, not a novelty. No token gymnastics. Source: xAI

Mario Nawfal

68,103 Aufrufe • vor 7 Monaten

🇺🇦 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.
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🇺🇦 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 Aufrufe • vor 4 Monaten

Antidote 🐸🧙‍♀️ Wizard Trees (Animal Face × Sin Mintz) No surprise. This is NOT Toad Venom, Frog Poison, or anything even remotely close to it. It's fucking Runtz Sad that this is actually being sold to anyone. The label itself even states the genetics as 'Animal Face × Sin Mintz', which is a lie. So, the wrong cut was acquired... They grew it, knew it was wrong, and STILL decided to move it?! What in the actual fuck. C'mon Scott
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Antidote 🐸🧙‍♀️ Wizard Trees (Animal Face × Sin Mintz) No surprise. This is NOT Toad Venom, Frog Poison, or anything even remotely close to it. It's fucking Runtz Sad that this is actually being sold to anyone. The label itself even states the genetics as 'Animal Face × Sin Mintz', which is a lie. So, the wrong cut was acquired... They grew it, knew it was wrong, and STILL decided to move it?! What in the actual fuck. C'mon Scott

Terps Tester

32,881 Aufrufe • vor 4 Monaten

This is how Palestinian journalism has been targeted for years: An occupation soldier opens the door of a military vehicle and fires dozens of bullets directly at a Palestinian journalist’s camera, simply because it conveys the truth. Sniping the lenses, silencing the image, and attempting to kill the truth… But the truth does not die, even if they smash the cameras.
0:55

This is how Palestinian journalism has been targeted for years: An occupation soldier opens the door of a military vehicle and fires dozens of bullets directly at a Palestinian journalist’s camera, simply because it conveys the truth. Sniping the lenses, silencing the image, and attempting to kill the truth… But the truth does not die, even if they smash the cameras.

Sameh Ahmed 𓂆 🇵🇸

211,476 Aufrufe • vor 7 Monaten

vibe coding metahuman with threejs and codex: spitting FACS ok, i'll admit 3D modeling has a lot of depth, going from a cube to a full facial action coding system is.. a lot of work, but really it isn't all that hard to learn like some people want you to believe it took me about 10 evenings to go from scratch (no idea how to even model in blender beyond the famous donut) to a working rigged and shape keyed expression system the actual vibe coding parts are very manageable, i'm not even hitting any session limits the past week since codex is basically just reading the glb file and it writes the drivers to have a full animation system in a single turn after that i usually notice that some things don't look as professional as i like it to be, followed by codex sending my ass back to blender and telling me what i need to do to make it better so here we are, the expression system is still very very simplified, no micro expressions yet and i believe i will need probably 500+ shape keys to actual achieve realism, but why not? Doing the 52 ARKit expressions for this demo wasn't too hard either, just tedious and boring The final driver in the demo uses a mix of bone movements and blendshapes to smoothly animate the face I might mix up some terminology here but i usually do cause it's been like what, 10 days since i started? Guess next up i'll look into face deformation parameters and then we'll move to the fun part of skinning. Once the head/neck area works (hairs are also still missing), full body next and finally LOD systems and we can call it a day Demo (pls use a desktop i'm not optimizing the ui for mobile):
0:41

vibe coding metahuman with threejs and codex: spitting FACS ok, i'll admit 3D modeling has a lot of depth, going from a cube to a full facial action coding system is.. a lot of work, but really it isn't all that hard to learn like some people want you to believe it took me about 10 evenings to go from scratch (no idea how to even model in blender beyond the famous donut) to a working rigged and shape keyed expression system the actual vibe coding parts are very manageable, i'm not even hitting any session limits the past week since codex is basically just reading the glb file and it writes the drivers to have a full animation system in a single turn after that i usually notice that some things don't look as professional as i like it to be, followed by codex sending my ass back to blender and telling me what i need to do to make it better so here we are, the expression system is still very very simplified, no micro expressions yet and i believe i will need probably 500+ shape keys to actual achieve realism, but why not? Doing the 52 ARKit expressions for this demo wasn't too hard either, just tedious and boring The final driver in the demo uses a mix of bone movements and blendshapes to smoothly animate the face I might mix up some terminology here but i usually do cause it's been like what, 10 days since i started? Guess next up i'll look into face deformation parameters and then we'll move to the fun part of skinning. Once the head/neck area works (hairs are also still missing), full body next and finally LOD systems and we can call it a day Demo (pls use a desktop i'm not optimizing the ui for mobile):

robot

29,357 Aufrufe • vor 3 Monaten