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Meet Lux3D from Manycore Tech - a new 3D generative model from Manycore Tech. Turn a single image or text prompt into a full set of editable 3D assets. No modeling skills required. But here's what makes it different: it's not just about quality. It's about cutting the cost... show more

Parul Gautam

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58,439 görüntüleme • 3 gün önce •via X (Twitter)

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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

92,867 görüntüleme • 2 yıl önce

Meta dropped not just SAM-3 yesterday but also SAM-3D (for objects and humans)🤯 SAM 3D Body is a promptable model for recovering full-body 3D human meshes from a single image. Seems to be the new SOTA

Meta dropped not just SAM-3 yesterday but also SAM-3D (for objects and humans)🤯 SAM 3D Body is a promptable model for recovering full-body 3D human meshes from a single image. Seems to be the new SOTA

Niels Rogge

125,235 görüntüleme • 9 ay önce

the fact that i can take an image of a room and turn it into a 3d model in one shot is actually insane this took like 30 seconds from image to 3d model

the fact that i can take an image of a room and turn it into a 3d model in one shot is actually insane this took like 30 seconds from image to 3d model

Jan

131,884 görüntüleme • 9 ay önce

Also the [ 🌐 Make 3d model ] feature on Photo AI has been upgraded to Tripo's new state of the art model So you can turn any AI photo or video you took into a 3d model in about a minute AI 3d models are not photorealistic yet, but as you see they're getting closer Before the face was completely distorted, now it's at least visible but still cartooney looking! 👍

Also the [ 🌐 Make 3d model ] feature on Photo AI has been upgraded to Tripo's new state of the art model So you can turn any AI photo or video you took into a 3d model in about a minute AI 3d models are not photorealistic yet, but as you see they're getting closer Before the face was completely distorted, now it's at least visible but still cartooney looking! 👍

@levelsio

47,093 görüntüleme • 1 yıl önce

Diffusion for virtual worlds: we've trained a new model to create 3D objects from text. And it's 50x faster than any alternative. We made each of these cute characters in just one minute with Genmo's new text-to-3D model. Build your own world at

Diffusion for virtual worlds: we've trained a new model to create 3D objects from text. And it's 50x faster than any alternative. We made each of these cute characters in just one minute with Genmo's new text-to-3D model. Build your own world at

Genmo

18,532 görüntüleme • 3 yıl önce

KIMI K3 JUST RECREATED A FUNCTIONAL GAME BOY ADVANCE IN 3D → interactive 3D model, every button and screen modeled → a game loaded and playable right inside it → camera you can rotate and zoom around the device → one single prompt, zero external assets $3 in API with K3. the same result would run about $9 with Fable 5 and $6 with Opus 4.8 this isn't generating a static image of hardware anymore, it's generating a working simulation of it, from the shell to the screen to the game running inside

KIMI K3 JUST RECREATED A FUNCTIONAL GAME BOY ADVANCE IN 3D → interactive 3D model, every button and screen modeled → a game loaded and playable right inside it → camera you can rotate and zoom around the device → one single prompt, zero external assets $3 in API with K3. the same result would run about $9 with Fable 5 and $6 with Opus 4.8 this isn't generating a static image of hardware anymore, it's generating a working simulation of it, from the shell to the screen to the game running inside

alex

38,248 görüntüleme • 1 ay önce

We are excited to introduce Stable Fast 3D, Stability AI’s latest breakthrough in 3D asset generation technology. This innovative model transforms a single input image into a detailed 3D asset in just 0.5 seconds, setting a new standard for speed and quality in the field of 3D reconstruction! Alongside this release, we’ve also published a technical report that highlights how we achieve fast inference speeds with reduced baked illumination and material parameters. 👾You can learn more and access the report here:

We are excited to introduce Stable Fast 3D, Stability AI’s latest breakthrough in 3D asset generation technology. This innovative model transforms a single input image into a detailed 3D asset in just 0.5 seconds, setting a new standard for speed and quality in the field of 3D reconstruction! Alongside this release, we’ve also published a technical report that highlights how we achieve fast inference speeds with reduced baked illumination and material parameters. 👾You can learn more and access the report here:

Stability AI

438,839 görüntüleme • 2 yıl önce

SAM 3D Body is a CVPR 2026 award candidate paper from AI at Meta model recovers a full 3D human body mesh from a single RGB image you can run it automatically, or guide the reconstruction with masks and 2D keypoints thx to Niels Rogge for awesome demo idea

SAM 3D Body is a CVPR 2026 award candidate paper from AI at Meta model recovers a full 3D human body mesh from a single RGB image you can run it automatically, or guide the reconstruction with masks and 2D keypoints thx to Niels Rogge for awesome demo idea

SkalskiP

72,500 görüntüleme • 2 ay önce

Meet Dreamina The way it creates videos, surprising.. You can now turn a single image or text prompt into a full cinematic AI video; no editing, no limits. This update is scary good. Here’s what I made with it 👇

Meet Dreamina The way it creates videos, surprising.. You can now turn a single image or text prompt into a full cinematic AI video; no editing, no limits. This update is scary good. Here’s what I made with it 👇

Fakhr

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I just made $850 on Upwork from a 3D model I didn't sculpt by hand. Here's what I actually did: • Created the character concept with AI • Dropped the image into RODIN Gen-2.5 A few moments later, it generated a production-ready 3D model with over 10 million polygons and incredibly detailed skin textures.

I just made $850 on Upwork from a 3D model I didn't sculpt by hand. Here's what I actually did: • Created the character concept with AI • Dropped the image into RODIN Gen-2.5 A few moments later, it generated a production-ready 3D model with over 10 million polygons and incredibly detailed skin textures.

Nyama Quarter

86,932 görüntüleme • 1 ay önce

Huawei's new 3D lock screen wallpaper is actually pretty interesting. Unlike iPhone's Spatial Photo, which creates a fixed stereoscopic depth effect from a photo, this lets you scan an object 360°, generate a full 3D Gaussian Splatting asset, and use that as your lock screen. Tilt the phone and you're moving through a reconstructed 3D scene, not a fixed stereo image. The part worth paying attention to: 3D capture moving out of creative pipelines and into everyday phone personalization. That's a different kind of adoption curve.

Huawei's new 3D lock screen wallpaper is actually pretty interesting. Unlike iPhone's Spatial Photo, which creates a fixed stereoscopic depth effect from a photo, this lets you scan an object 360°, generate a full 3D Gaussian Splatting asset, and use that as your lock screen. Tilt the phone and you're moving through a reconstructed 3D scene, not a fixed stereo image. The part worth paying attention to: 3D capture moving out of creative pipelines and into everyday phone personalization. That's a different kind of adoption curve.

KIRI Engine - 3D Scanner App

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Whatever you’re building, a game, an animation, or a world of your own, 404 helps you turn ideas into usable 3D assets. Build beautiful organic worlds using 404-generated assets. Every asset in this reel was generated with 404. All you bring is the prompt. Try it out at:

Whatever you’re building, a game, an animation, or a world of your own, 404 helps you turn ideas into usable 3D assets. Build beautiful organic worlds using 404-generated assets. Every asset in this reel was generated with 404. All you bring is the prompt. Try it out at:

404

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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 görüntüleme • 2 yıl önce

NVIDIA finally released Neuralangelo's source code! The model can turn videos from any device into detailed 3D structures, fully replicating buildings, sculptures, or other real aworld objects or spaces virtually. Here's how it works: A model utilizes a 2D video with multiple angles of an object or scene. I selects frames from different viewpoints to understand depth, size, and shape. The AI creates an initial 3D representation, similar to a sculptor shaping a subject. The render is optimized to enhance details, like a sculptor refining texture. The outcome is a 3D object or scene suitable for virtual reality, digital twins, or robotics.

NVIDIA finally released Neuralangelo's source code! The model can turn videos from any device into detailed 3D structures, fully replicating buildings, sculptures, or other real aworld objects or spaces virtually. Here's how it works: A model utilizes a 2D video with multiple angles of an object or scene. I selects frames from different viewpoints to understand depth, size, and shape. The AI creates an initial 3D representation, similar to a sculptor shaping a subject. The render is optimized to enhance details, like a sculptor refining texture. The outcome is a 3D object or scene suitable for virtual reality, digital twins, or robotics.

Lior Alexander

478,069 görüntüleme • 3 yıl önce

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 görüntüleme • 1 yıl önce

This isn’t a 3D model thrown in a box. This is pure code. No Blender, no Maya, no shortcuts. Every voxel, every jump, every movement scripted from scratch. Generative art at this level means thinking like a designer, a coder, and an artist all at once. It’s not about placing pixels. It’s about writing the rules of how a frog should exist in the Bitcoin chain. BLOCKFROGS isn’t just generative. It’s engineered. Be part of this experience Wl = 2 Freemints 5 WL to snatch Post or Quote about BLOCK | Frogs | Panthers Drop the link

This isn’t a 3D model thrown in a box. This is pure code. No Blender, no Maya, no shortcuts. Every voxel, every jump, every movement scripted from scratch. Generative art at this level means thinking like a designer, a coder, and an artist all at once. It’s not about placing pixels. It’s about writing the rules of how a frog should exist in the Bitcoin chain. BLOCKFROGS isn’t just generative. It’s engineered. Be part of this experience Wl = 2 Freemints 5 WL to snatch Post or Quote about BLOCK | Frogs | Panthers Drop the link

BLOCK | Frogs | Panthers

16,246 görüntüleme • 1 yıl 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 • 25 gün önce

Experimenting with image-to-world: creating consistent 3D environments from a single photo. As someone who studied architecture, this feels like a glimpse into the future of spatial creation and I’m excited to see where World Labs takes it.

Experimenting with image-to-world: creating consistent 3D environments from a single photo. As someone who studied architecture, this feels like a glimpse into the future of spatial creation and I’m excited to see where World Labs takes it.

Ian Curtis

61,754 görüntüleme • 1 yıl önce