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8. CUSTOMIZABLE HEAD MODEL dont think many people know this unless they check csp updates (its more of a recent feature) but the default head model is completely customizable and it’s awesome!! can choose from presets or adjust features manually materials > 3D > basic

KITTY★DISK

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35,529 görüntüleme • 1 yıl önce •via X (Twitter)

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Our customizable 3D vtuber model now has hair and transparency!! :D We haven't seen any Live2D styled customizable models made for 3D yet, so we want to make a high quality model with lots of tech and features! -^u^- If you're interested, please let us know with VGen's 'get notified' feature!✨It would make all the effort Palette and I are putting into this all the more worthwhile!

Our customizable 3D vtuber model now has hair and transparency!! :D We haven't seen any Live2D styled customizable models made for 3D yet, so we want to make a high quality model with lots of tech and features! -^u^- If you're interested, please let us know with VGen's 'get notified' feature!✨It would make all the effort Palette and I are putting into this all the more worthwhile!

Pastell & Palette🤍 3D Customizable Model!

98,115 görüntüleme • 1 yıl önce

🚨 Wait… I think I just discovered something HUGE about AI-generated 3D models. I found Hyper3D’s new Agentic Mode that can BUILD, EDIT & ANIMATE 3D models!! And there’s one thing that really surprised me. Unlike regular 3D generation, Agentic Mode can automatically filter and organize more informative input. You can give it different views, individual parts, or even a single reference image of separate components… And it can figure out how they fit together and generate a complete 3D model — with surprisingly accurate structure and details. That’s something I haven’t seen regular 3D generation handle this well. Check out this 3D windmill animation 👇 Hyper3D Agentic Mode lets you control 3D models with PARAMETERS !! Instead of rebuilding the sofa from scratch, you can change things like: → Parametric Control → Animation Pressets → Enhance Materials So you can generate the model once... and keep editing its geometry without starting from scratch

🚨 Wait… I think I just discovered something HUGE about AI-generated 3D models. I found Hyper3D’s new Agentic Mode that can BUILD, EDIT & ANIMATE 3D models!! And there’s one thing that really surprised me. Unlike regular 3D generation, Agentic Mode can automatically filter and organize more informative input. You can give it different views, individual parts, or even a single reference image of separate components… And it can figure out how they fit together and generate a complete 3D model — with surprisingly accurate structure and details. That’s something I haven’t seen regular 3D generation handle this well. Check out this 3D windmill animation 👇 Hyper3D Agentic Mode lets you control 3D models with PARAMETERS !! Instead of rebuilding the sofa from scratch, you can change things like: → Parametric Control → Animation Pressets → Enhance Materials So you can generate the model once... and keep editing its geometry without starting from scratch

SARAH

90,925 görüntüleme • 2 gün önce

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:

Another big drop from Hunyuan3D by Tencent. Hunyuan3D-Buffalo 1.0 is not just another text-to-3D model. The more interesting part is its approach to 3D understanding and editing. It can understand separate parts of a complete asset, extract them, remove or replace them with prompts, and edit only the selected region while keeping the rest of the geometry unchanged. That feels like a much more useful direction for 3D AI in general: not just generating a mesh once, but actually understanding its structure and letting you continue working with it. The model combines Qwen-VL, TRELLIS and Hunyuan3D, and was trained on an impressive dataset of 87 million 3D samples. Source:

Stefan 3D AI

36,034 görüntüleme • 1 ay önce

There are way too many AI 3D models claiming to be the best. So we’re testing them head-to-head. Tripo P1 vs Meshy V6 vs Hunyuan V3.1 Pro vs Rodin 2.5 Same image. Same prompt. No cherry-picking, only 1 generation. This week’s test: a shiny metal helmet. reflections, materials, geometry, and texture accuracy. Next up: characters, objects, environments, and more. By the end, we want one answer: Which AI 3D model should you actually use for your next game, website, or project?

There are way too many AI 3D models claiming to be the best. So we’re testing them head-to-head. Tripo P1 vs Meshy V6 vs Hunyuan V3.1 Pro vs Rodin 2.5 Same image. Same prompt. No cherry-picking, only 1 generation. This week’s test: a shiny metal helmet. reflections, materials, geometry, and texture accuracy. Next up: characters, objects, environments, and more. By the end, we want one answer: Which AI 3D model should you actually use for your next game, website, or project?

mint

21,547 görüntüleme • 1 ay önce

I know this is all over X but it’s fascinating. From #MeghanMarkle’s hand on the back of Harry’s neck forcing his head down to his obvious disinterest in kissing her-he’s far more interested in what’s in his bowl-there’s a lack of warmth between the 2 and a disconnect. You don’t see this interaction and think “this is a couple that is so in love”. It’s all performative. #WithLoveMeghan #HarryandMeghan

I know this is all over X but it’s fascinating. From #MeghanMarkle’s hand on the back of Harry’s neck forcing his head down to his obvious disinterest in kissing her-he’s far more interested in what’s in his bowl-there’s a lack of warmth between the 2 and a disconnect. You don’t see this interaction and think “this is a couple that is so in love”. It’s all performative. #WithLoveMeghan #HarryandMeghan

Princess CarParkle 👑

140,823 görüntüleme • 10 ay önce

Many people struggle with this “truth” … so many of you are struggling to draw the line, tell the truth … especially if it’s to someone you love or admire. This gentleman and many more in line at #veecon spoke on their inability to deliver a pushback .. I know it’s hard but candor when done kindly is a gift and it shouldn’t be confused with being angry or mean …

Many people struggle with this “truth” … so many of you are struggling to draw the line, tell the truth … especially if it’s to someone you love or admire. This gentleman and many more in line at #veecon spoke on their inability to deliver a pushback .. I know it’s hard but candor when done kindly is a gift and it shouldn’t be confused with being angry or mean …

Gary Vaynerchuk

35,245 görüntüleme • 2 yıl önce

Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.

Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.

KIRI Engine - 3D Scanner App

12,263 görüntüleme • 4 ay önce

The low-quality dummy model of the Samsung Galaxy Z Fold 8 Wide has already appeared online. Its build quality is far from the real device, so it should only be used as a rough reference. From what I know, Samsung’s internal testers are actually very fond of this device. Maybe it’s because the form factor feels fresh and different.

The low-quality dummy model of the Samsung Galaxy Z Fold 8 Wide has already appeared online. Its build quality is far from the real device, so it should only be used as a rough reference. From what I know, Samsung’s internal testers are actually very fond of this device. Maybe it’s because the form factor feels fresh and different.

Ice Universe

106,113 görüntüleme • 4 ay önce

I know I’ve joked about it a lot But seeing this on the big screen in an actual cinema full of people was pretty awesome :D Met so many cool ppl And have had sm more opportunities come from it all Super excited to share the next side quest, sooner than you’d think lmao

I know I’ve joked about it a lot But seeing this on the big screen in an actual cinema full of people was pretty awesome :D Met so many cool ppl And have had sm more opportunities come from it all Super excited to share the next side quest, sooner than you’d think lmao

Lukey

104,505 görüntüleme • 1 yıl önce

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

50% cheaper Claude inference with just one line of code change! - Remove → model="claude-opus-4-8" - Add → model="ship-like/claude-opus-4-8" I verified the cost saving in my own terminal by invoking the same Anthropic model with the same prompt. The underlying engineering by Ship is actually interesting, and the patterns can be used in any production LLM stack. Essentially, a trained model is a frozen artifact. Every request performs the same forward-pass, whether it extracts a date or refactors a module, because the compute decision was made at training time, before the request existed. Ship makes that decision at inference time instead. After seeing a request, it searches over executions, involving single models, cascades, ensembles, or harnesses with tools, and serves the cheapest one that will match the reference model's quality. This is not a basic router, because picking a cheaper model per query doesn't ensure the cheaper model preserves the original's behavior, like output shape, tool-call patterns, and refusals. Ship measures this equivalence directly. Outputs stay distributionally indistinguishable from the reference model, not token-identical, since two calls to the same model already differ, but they are indistinguishable in capability and behavior. Of course, some requests execute cheaply and some cost Ship more than the customer pays, but the price per request is still a flat 50% off either way, so the execution-cost variance moves off the application's bill entirely. The video below depicts the cost savings and output in my real invocation, and I partnered with the team to put this together.

Akshay 🚀

64,482 görüntüleme • 2 ay önce

HUGE UPCOMING UPDATE FOR OUR 3D CUSTOMIZABLE! ✨🩷This will be out very soon! (I had a lot of computer problems, so sorry!!) The upcoming update will include: - A chibi toggle (+ other body types too!) - Presets for outfits, hair styles, and body toggles! - Many bug fixes! -^u^- - Descriptions for every customization slider + helpful tips - A much cleaner + easier to navigate UI! - MMD shape keys + vowels for audio tracking - New expressions (>_<, dot eyes, and bleps!) - FPS improvements and optimizations for quicker scene saving - Auto saves - Gradients now work better + the direction can be changed! - An integrated tutorial for users that are new to Warudo + a guide on how to customize the model

HUGE UPCOMING UPDATE FOR OUR 3D CUSTOMIZABLE! ✨🩷This will be out very soon! (I had a lot of computer problems, so sorry!!) The upcoming update will include: - A chibi toggle (+ other body types too!) - Presets for outfits, hair styles, and body toggles! - Many bug fixes! -^u^- - Descriptions for every customization slider + helpful tips - A much cleaner + easier to navigate UI! - MMD shape keys + vowels for audio tracking - New expressions (>_<, dot eyes, and bleps!) - FPS improvements and optimizations for quicker scene saving - Auto saves - Gradients now work better + the direction can be changed! - An integrated tutorial for users that are new to Warudo + a guide on how to customize the model

Pastell & Palette🤍 3D Customizable Model!

27,006 görüntüleme • 1 ay önce

American got a BBQ sandwich at a gas station and took it home to microwave it Shockingly the BBQ sauce isn’t a sauce at all, it’s transformed into a jelly like patty It’s a near solid, keeps its shape and flops around From what I can find it seems like many gas stations use commercial bbq sauces full of thickeners, they also use corn syrup and high high-fructose corn syrup combined can create this jelly Other people online think it’s 3D printed, less likely Our food is a science experiment. We have to Make America Healthy Again

American got a BBQ sandwich at a gas station and took it home to microwave it Shockingly the BBQ sauce isn’t a sauce at all, it’s transformed into a jelly like patty It’s a near solid, keeps its shape and flops around From what I can find it seems like many gas stations use commercial bbq sauces full of thickeners, they also use corn syrup and high high-fructose corn syrup combined can create this jelly Other people online think it’s 3D printed, less likely Our food is a science experiment. We have to Make America Healthy Again

Wall Street Apes

91,603 görüntüleme • 5 ay önce

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..

not sure why nobody is talking about this but Google Omni is insane at video editing Original Video (left) vs Omni Edited Video (right) everyone is comparing it to Seedance and missing the point completely. Seedance is for generating videos from scratch. Google Omni is for editing videos that already exist. which are two completely different use cases this is like when Nano Banana 1 first came out and nobody realized how big it was going to be. this is the first AI that can actually properly edit videos.. i've generated a few hundred videos with this model and it can do literally any type of edit you can think of. changing voices, swapping characters, removing watermarks, adding captions, transitions, pop ups, whatever. if you can describe the edit you want it can do it this completely crushes every other model on the market when it comes to video editing. nothing else even comes close right now and this is just the flash model. imagine what the pro version is going to be able to do when it drops in a couple months this should have way more hype than it's getting..

Miko

30,156 görüntüleme • 4 ay önce

Google DeepMind is doing some crazy work. This is so good and is going to help so many people. They just launched SL2T (Sign Language to Text), an AI model that translates sign language directly into text. Most spoken language dictation tools rely on audio to text, but sign languages have their own grammars and involve complex 3D movements. Instead of using raw camera feeds or physical gloves, SL2T tracks body landmark coordinates locally with MediaPipe Holistic and translates those spatial points into text. It’s powering sign to text dictation on Pixel 11 in Gboard and Live Transcribe, starting with ASL. Users can sign naturally to search, draft messages, or respond in live conversations instead of typing everything out. This is seriously awesome.

Google DeepMind is doing some crazy work. This is so good and is going to help so many people. They just launched SL2T (Sign Language to Text), an AI model that translates sign language directly into text. Most spoken language dictation tools rely on audio to text, but sign languages have their own grammars and involve complex 3D movements. Instead of using raw camera feeds or physical gloves, SL2T tracks body landmark coordinates locally with MediaPipe Holistic and translates those spatial points into text. It’s powering sign to text dictation on Pixel 11 in Gboard and Live Transcribe, starting with ASL. Users can sign naturally to search, draft messages, or respond in live conversations instead of typing everything out. This is seriously awesome.

AshutoshShrivastava

27,256 görüntüleme • 1 ay önce

If you typically stream with a 3D model, I highly recommend that you pose your model when you’re not doing full body mocap instead of letting it stay in the stiff generic pose! It makes a huge difference in how your energy is conveyed (ᗒ⩊ᗕ)⸝ި ʕᦏ⌎ I’m only saying this because I noticed this a lot with my own 3D kids, but here’s a comparison showcase: ← default pose & default pendulum physics in Warudo → custom poses & slightly adjusted pendulum physics If you find it hard to pose within Warudo by using bone offsets or none of the existing poses in Warudo vibe with you, you can make your own like me! I made my custom poses in “VRM Posing Desktop” (this is the BEST vrm posing app I’ve ever used in the past 3 years) and exported them as Unity anims then dropped them into Warudo’s animation folder! You can then make a simple blueprint in Warudo to toggle between poses and make your model look more alive! It should fit especially well for just chatting streams 🙂‍↕️✨

If you typically stream with a 3D model, I highly recommend that you pose your model when you’re not doing full body mocap instead of letting it stay in the stiff generic pose! It makes a huge difference in how your energy is conveyed (ᗒ⩊ᗕ)⸝ި ʕᦏ⌎ I’m only saying this because I noticed this a lot with my own 3D kids, but here’s a comparison showcase: ← default pose & default pendulum physics in Warudo → custom poses & slightly adjusted pendulum physics If you find it hard to pose within Warudo by using bone offsets or none of the existing poses in Warudo vibe with you, you can make your own like me! I made my custom poses in “VRM Posing Desktop” (this is the BEST vrm posing app I’ve ever used in the past 3 years) and exported them as Unity anims then dropped them into Warudo’s animation folder! You can then make a simple blueprint in Warudo to toggle between poses and make your model look more alive! It should fit especially well for just chatting streams 🙂‍↕️✨

𝗞𝗔𝗥𝗜𝗛𝗔 🌘🍀 𝟯𝗗 𝗔𝗿𝘁𝗶𝘀𝘁 ☻

16,214 görüntüleme • 7 ay önce

If you think this is just another silly demo made with AI, read this post. You might change your mind, because this demo is about MATH. What you see on the screen is not a render from Blender (obviously, it’s not that good). It’s a three.js app built with Toolcraft. Available on the web and rendered in real time(link in the comments). But Blender still has a lot to do with it. Blender has Geometry Nodes - a powerful node-based system for creating and manipulating procedural geometry. In other words, it’s math. And math is a universal language. And who do you think is pretty good at math? >>> AI. Now you can download or buy Blender files from marketplaces, and when they contain Geometry Nodes for procedural animations, objects, surfaces, or effects, you can transfer that logic to the web. Make it real-time, make it interactive. Materials are a separate story, of course. They can still suck unless you use the right tricks: PBR, HDRIs, material blending, displacement, and faked surface relief. So why is Blender important here? Blender is open source, and many tools around it are open source too. An AI trained on their code. That means it can translate the math from one environment to another quite accurately. If you’ve been struggling to reproduce some idea with AI that you had in your head or seen in some references, and it has something to do with Geometry Nodes, and you can find that idea or a close one in the Blender ecosystem - it means you can transfer it to the web. Thank me later.

If you think this is just another silly demo made with AI, read this post. You might change your mind, because this demo is about MATH. What you see on the screen is not a render from Blender (obviously, it’s not that good). It’s a three.js app built with Toolcraft. Available on the web and rendered in real time(link in the comments). But Blender still has a lot to do with it. Blender has Geometry Nodes - a powerful node-based system for creating and manipulating procedural geometry. In other words, it’s math. And math is a universal language. And who do you think is pretty good at math? >>> AI. Now you can download or buy Blender files from marketplaces, and when they contain Geometry Nodes for procedural animations, objects, surfaces, or effects, you can transfer that logic to the web. Make it real-time, make it interactive. Materials are a separate story, of course. They can still suck unless you use the right tricks: PBR, HDRIs, material blending, displacement, and faked surface relief. So why is Blender important here? Blender is open source, and many tools around it are open source too. An AI trained on their code. That means it can translate the math from one environment to another quite accurately. If you’ve been struggling to reproduce some idea with AI that you had in your head or seen in some references, and it has something to do with Geometry Nodes, and you can find that idea or a close one in the Blender ecosystem - it means you can transfer it to the web. Thank me later.

Alex Barashkov

28,300 görüntüleme • 2 ay önce