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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:show more

SpAItial AI

4,834 subscribers

177,286 Aufrufe • vor 10 Monaten •via X (Twitter)

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37 Kommentare

Profilbild von Rafael Torres ᯅ

Rafael Torres ᯅvor 10 Monaten

I found the model amazing — it just needs to be enabled for viewing on VR devices.

Profilbild von Andre Retterath | Decoding Outliers

Andre Retterath | Decoding Outliersvor 10 Monaten

Team, huge congrats for releasing the model only 6 months after the Seed round!! 2026 will be 🔥🔥

Profilbild von Alex Finn

Alex Finnvor 10 Monaten

Can i wire these up in Unity?

Profilbild von SpAItial AI

SpAItial AIvor 10 Monaten

Yes, the outputs are distilled into 3dgs representations and can easily be integrated!

Profilbild von Philipp Henzler

Philipp Henzlervor 10 Monaten

Congrats!!!

Profilbild von Jason W - AI

Jason W - AIvor 10 Monaten

Are you able to put people or characters into the space?

Profilbild von MechatronicsEngineer

MechatronicsEngineervor 10 Monaten

@Scobleizer Who owns the images/videos produced?

Profilbild von Akash Bajwa

Akash Bajwavor 10 Monaten

So excited for what's to come!

Profilbild von SpAItial AI

SpAItial AIvor 10 Monaten

We'll change the world, obviously 🔥🔥🔥

Profilbild von 𝐌𝐫. 𝐖𝐢𝐜𝐤 🇺🇸 🦍

𝐌𝐫. 𝐖𝐢𝐜𝐤 🇺🇸 🦍vor 10 Monaten

Now if we were to integrate VR and Voice prompting then we're almost at the stage where you can walk into your world, point and change.

Profilbild von PS

PSvor 10 Monaten

@Scobleizer I wrote a script 10 years ago about such world. One of the rules: no option to remove an item as it may impose a risk of harm to a user (real world). I would be ok to add anything to a specific not reachable areas. Some areas (interior/ exterior would consist only of dummies

Profilbild von Genex

Genexvor 10 Monaten

Congrats!

Profilbild von MetaDJ

MetaDJvor 10 Monaten

Amazing ✨

Profilbild von Joshua Johnson

Joshua Johnsonvor 10 Monaten

Will definitely be checking this out!

Profilbild von JB 🇺🇸

JB 🇺🇸vor 10 Monaten

Are the assets downloadable for modeling programs if edits are wanted?

Profilbild von Himanshu Kumar

Himanshu Kumarvor 10 Monaten

3D world generation model yields coherent spatial representation from text.

Profilbild von Chukwudalu Dumebi-Kachikwu

Chukwudalu Dumebi-Kachikwuvor 10 Monaten

This is incredible

Profilbild von 𝕋ℝ𝕆𝕐 𝕠𝕗 𝕏

𝕋ℝ𝕆𝕐 𝕠𝕗 𝕏vor 10 Monaten

@jon_barron WATTBA!

Profilbild von Sakib

Sakibvor 10 Monaten

Can’t wait for my first braindance

Profilbild von 𝐙𝐞𝐧𝐠 💜

𝐙𝐞𝐧𝐠 💜vor 10 Monaten

Impressive!

Profilbild von SpAItial AI

SpAItial AIvor 10 Monaten

Thanks!

Profilbild von Jakub Červený

Jakub Červenývor 10 Monaten

Cool model, but you need a better viewer:

Profilbild von RThomas 🚀🇿🇦🏁

RThomas 🚀🇿🇦🏁vor 10 Monaten

What other sources can be used? 360 Photos? Have played around with Gaussian Splats lately, VR interaction? three.js?

Profilbild von Arseni Santashev

Arseni Santashevvor 10 Monaten

API soon?

Profilbild von Ai Machete

Ai Machetevor 10 Monaten

Incredible! 👀

Profilbild von Anders Hjemdahl

Anders Hjemdahlvor 10 Monaten

Absolutely incredible work, congratulations on getting all the way to this point - and can't wait to see how this improves! Can you combine splats with collisions (underlying mesh)?

Profilbild von Hey

Heyvor 10 Monaten

This will be so useful for home remodelling services. Gives non visual people a way to actually see what the room would look like instead of just pics. Would it be possible to extract pics out of this model when flipped to a different angle/direction?

Profilbild von kano

kanovor 7 Monaten

This level of detail from a single image prompt is impressive. The normal maps and textures look solid.

Profilbild von Hey

Heyvor 10 Monaten

@levelsio is this something you're trying to set up too?

Profilbild von おさ

おさvor 10 Monaten

Hello. We've been experiencing high volumes of traffic, causing the site to slow down. My computer has low specs, so the sample file has stopped working and is now buried in the floor of the room. I hope that it will eventually run smoothly even on low-spec computers.

Profilbild von PS

PSvor 10 Monaten

What's the maximum resolution you can output? I wonder if this could be used for various scenarios in a movie production.

Profilbild von Jikku Jose

Jikku Josevor 10 Monaten

Real time? On what hardware?

Profilbild von Mr. Hackathon

Mr. Hackathonvor 10 Monaten

Waitlit?

Profilbild von 17 years of song a day

17 years of song a dayvor 10 Monaten

please let me in!

Profilbild von Vadim

Vadimvor 10 Monaten

That’s a lot of EM dashes 😂

Profilbild von Hyperstack

Hyperstackvor 10 Monaten

Impressive!

Profilbild von Joe Williams

Joe Williamsvor 10 Monaten

That’s insane. I love it 😍

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Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Tencent Hy

168,984 Aufrufe • vor 1 Jahr

With Insta360's Spatial Capture officially released in the US today, we can finally announce our partnership with Insta360. Yes — Insta360’s Spatial Capture feature is powered by KIRI Engine. By combining 360° cameras with KIRI Engine’s 3D Gaussian Splatting technology, we’re making it dramatically easier to capture large real-world environments and turn them into immersive 3D spaces. One of the biggest breakthroughs here is the capture workflow. Compared with existing 360° Gaussian Splatting workflows, Spatial Capture is dramatically simpler We believe 360 cameras can become one of the most natural ways to capture the world in 3D — and this collaboration is a big step toward making that happen.

With Insta360's Spatial Capture officially released in the US today, we can finally announce our partnership with Insta360. Yes — Insta360’s Spatial Capture feature is powered by KIRI Engine. By combining 360° cameras with KIRI Engine’s 3D Gaussian Splatting technology, we’re making it dramatically easier to capture large real-world environments and turn them into immersive 3D spaces. One of the biggest breakthroughs here is the capture workflow. Compared with existing 360° Gaussian Splatting workflows, Spatial Capture is dramatically simpler We believe 360 cameras can become one of the most natural ways to capture the world in 3D — and this collaboration is a big step toward making that happen.

KIRI Engine - 3D Scanner App

18,241 Aufrufe • vor 26 Tagen

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior paper page: present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation.

AK

161,530 Aufrufe • vor 3 Jahren

LayerPano3D Layered 3D Panorama for Hyper-Immersive Scene Generation discuss: 3D immersive scene generation is a challenging yet critical task in computer vision and graphics. A desired virtual 3D scene should 1) exhibit omnidirectional view consistency, and 2) allow for free exploration in complex scene hierarchies. Existing methods either rely on successive scene expansion via inpainting or employ panorama representation to represent large FOV scene environments. However, the generated scene suffers from semantic drift during expansion and is unable to handle occlusion among scene hierarchies. To tackle these challenges, we introduce LayerPano3D, a novel framework for full-view, explorable panoramic 3D scene generation from a single text prompt. Our key insight is to decompose a reference 2D panorama into multiple layers at different depth levels, where each layer reveals the unseen space from the reference views via diffusion prior. LayerPano3D comprises multiple dedicated designs: 1) we introduce a novel text-guided anchor view synthesis pipeline for high-quality, consistent panorama generation. 2) We pioneer the Layered 3D Panorama as underlying representation to manage complex scene hierarchies and lift it into 3D Gaussians to splat detailed 360-degree omnidirectional scenes with unconstrained viewing paths. Extensive experiments demonstrate that our framework generates state-of-the-art 3D panoramic scene in both full view consistency and immersive exploratory experience. We believe that LayerPano3D holds promise for advancing 3D panoramic scene creation with numerous applications.

LayerPano3D Layered 3D Panorama for Hyper-Immersive Scene Generation discuss: 3D immersive scene generation is a challenging yet critical task in computer vision and graphics. A desired virtual 3D scene should 1) exhibit omnidirectional view consistency, and 2) allow for free exploration in complex scene hierarchies. Existing methods either rely on successive scene expansion via inpainting or employ panorama representation to represent large FOV scene environments. However, the generated scene suffers from semantic drift during expansion and is unable to handle occlusion among scene hierarchies. To tackle these challenges, we introduce LayerPano3D, a novel framework for full-view, explorable panoramic 3D scene generation from a single text prompt. Our key insight is to decompose a reference 2D panorama into multiple layers at different depth levels, where each layer reveals the unseen space from the reference views via diffusion prior. LayerPano3D comprises multiple dedicated designs: 1) we introduce a novel text-guided anchor view synthesis pipeline for high-quality, consistent panorama generation. 2) We pioneer the Layered 3D Panorama as underlying representation to manage complex scene hierarchies and lift it into 3D Gaussians to splat detailed 360-degree omnidirectional scenes with unconstrained viewing paths. Extensive experiments demonstrate that our framework generates state-of-the-art 3D panoramic scene in both full view consistency and immersive exploratory experience. We believe that LayerPano3D holds promise for advancing 3D panoramic scene creation with numerous applications.

AK

41,891 Aufrufe • vor 2 Jahren

AI 3D is getting much better at turning ideas into complete scenes. I’ve been checking out SEELE AI’s 3D Agent, and the part I found most interesting is how quickly it can turn a prompt or creative idea into a detailed 3D scene. Instead of generating a single isolated object, SeeleAgent can build out the broader environment and 3D models while keeping the result closely aligned with the original concept. That makes it much more useful for quickly turning an idea into a high-quality 3D scene without building everything manually. Try SeeleAgent here 👇 #seele #seeleai #3d

AI 3D is getting much better at turning ideas into complete scenes. I’ve been checking out SEELE AI’s 3D Agent, and the part I found most interesting is how quickly it can turn a prompt or creative idea into a detailed 3D scene. Instead of generating a single isolated object, SeeleAgent can build out the broader environment and 3D models while keeping the result closely aligned with the original concept. That makes it much more useful for quickly turning an idea into a high-quality 3D scene without building everything manually. Try SeeleAgent here 👇 #seele #seeleai #3d

Liam | AI Tools & News

14,722 Aufrufe • vor 2 Monaten

At Avalon we are building "Real-time creating" - the ability to generate gameplay ready persistent worlds prompted from text. While others are building real-time video world models, Avalon is building real-time world generation inside a fully playable, persistent multiplayer engine. Internally running at 3840×2180 at 60 FPS. Built on Unreal Engine. Multiplayer by default. Persistent by default. Gameplay-ready by default. This is not a video latent replay. Not a simulation of interaction. It is a real 3D world with physics, logic, and authoritative multiplayer state. Avalon is trained on proprietary Avalon interaction data and powered by a hybrid system that combines language understanding, 3D model generation, procedural systems, and structured gameplay logic synthesis. Players can walk through a live world and generate environments, assets, mechanics, and entirely new gameplay modes using natural language. We accomplish this through a combination of 3D model generation, game logic generation based on our proprietary systems, and AI driven world creation. While other players are inside it. Changes persist instantly. State is synchronized in real time. Creation happens inside the world, not outside of it. Describe a biome. Spawn a civilization. Create a survival mode. Build a dungeon crawler. Launch a new game inside the world. Avalon interprets intent and integrates it directly into the live multiplayer environment. This is not a world model predicting video. This is a gameplay engine that understands language. If you can describe it, you can build it. And others can walk into it instantly.

At Avalon we are building "Real-time creating" - the ability to generate gameplay ready persistent worlds prompted from text. While others are building real-time video world models, Avalon is building real-time world generation inside a fully playable, persistent multiplayer engine. Internally running at 3840×2180 at 60 FPS. Built on Unreal Engine. Multiplayer by default. Persistent by default. Gameplay-ready by default. This is not a video latent replay. Not a simulation of interaction. It is a real 3D world with physics, logic, and authoritative multiplayer state. Avalon is trained on proprietary Avalon interaction data and powered by a hybrid system that combines language understanding, 3D model generation, procedural systems, and structured gameplay logic synthesis. Players can walk through a live world and generate environments, assets, mechanics, and entirely new gameplay modes using natural language. We accomplish this through a combination of 3D model generation, game logic generation based on our proprietary systems, and AI driven world creation. While other players are inside it. Changes persist instantly. State is synchronized in real time. Creation happens inside the world, not outside of it. Describe a biome. Spawn a civilization. Create a survival mode. Build a dungeon crawler. Launch a new game inside the world. Avalon interprets intent and integrates it directly into the live multiplayer environment. This is not a world model predicting video. This is a gameplay engine that understands language. If you can describe it, you can build it. And others can walk into it instantly.

AVALON

66,475 Aufrufe • vor 8 Monaten

World Labs' Dr. Fei-Fei Li on why the 3D world and spatial intelligence are fundamentally different from language models: "Language is fundamentally a purely generated signal. There's no language out there. You don't go out in the nature and there's words written in the sky for you." "Whatever data you feed in, you pretty much can just somehow regurgitate with enough generalizability the same data out, and that's language to language." "But the 3D world is not. There is a 3D world out there that follows laws of physics, that has its own structures due to materials and many other things. And to fundamentally back that information out and be able to represent it and be able to generate it is just fundamentally quite a different problem." "We will be borrowing similar ideas or useful ideas from language and LLMs, but this is fundamentally philosophically, to me, a different problem." Fei-Fei Li

World Labs' Dr. Fei-Fei Li on why the 3D world and spatial intelligence are fundamentally different from language models: "Language is fundamentally a purely generated signal. There's no language out there. You don't go out in the nature and there's words written in the sky for you." "Whatever data you feed in, you pretty much can just somehow regurgitate with enough generalizability the same data out, and that's language to language." "But the 3D world is not. There is a 3D world out there that follows laws of physics, that has its own structures due to materials and many other things. And to fundamentally back that information out and be able to represent it and be able to generate it is just fundamentally quite a different problem." "We will be borrowing similar ideas or useful ideas from language and LLMs, but this is fundamentally philosophically, to me, a different problem." Fei-Fei Li

a16z

53,507 Aufrufe • vor 25 Tagen