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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...

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SpAItial AI

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177,286 görüntüleme • 10 ay önce •via X (Twitter)

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Rafael Torres ᯅ profil fotoğrafı
Rafael Torres ᯅ10 ay önce

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

Andre Retterath | Decoding Outliers profil fotoğrafı
Andre Retterath | Decoding Outliers10 ay önce

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

Alex Finn profil fotoğrafı
Alex Finn10 ay önce

Can i wire these up in Unity?

SpAItial AI profil fotoğrafı
SpAItial AI10 ay önce

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

Philipp Henzler profil fotoğrafı
Philipp Henzler10 ay önce

Congrats!!!

Jason W - AI profil fotoğrafı
Jason W - AI10 ay önce

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

MechatronicsEngineer profil fotoğrafı
MechatronicsEngineer10 ay önce

@Scobleizer Who owns the images/videos produced?

Akash Bajwa profil fotoğrafı
Akash Bajwa10 ay önce

So excited for what's to come!

SpAItial AI profil fotoğrafı
SpAItial AI10 ay önce

We'll change the world, obviously 🔥🔥🔥

𝐌𝐫. 𝐖𝐢𝐜𝐤 🇺🇸 🦍 profil fotoğrafı
𝐌𝐫. 𝐖𝐢𝐜𝐤 🇺🇸 🦍10 ay önce

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.

PS profil fotoğrafı
PS10 ay önce

@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

Genex profil fotoğrafı
Genex10 ay önce

Congrats!

MetaDJ profil fotoğrafı
MetaDJ10 ay önce

Amazing ✨

Joshua Johnson profil fotoğrafı
Joshua Johnson10 ay önce

Will definitely be checking this out!

JB 🇺🇸 profil fotoğrafı
JB 🇺🇸10 ay önce

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

Himanshu Kumar profil fotoğrafı
Himanshu Kumar10 ay önce

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

Chukwudalu Dumebi-Kachikwu profil fotoğrafı
Chukwudalu Dumebi-Kachikwu10 ay önce

This is incredible

𝕋ℝ𝕆𝕐 𝕠𝕗 𝕏 profil fotoğrafı
𝕋ℝ𝕆𝕐 𝕠𝕗 𝕏10 ay önce

@jon_barron WATTBA!

Sakib profil fotoğrafı
Sakib10 ay önce

Can’t wait for my first braindance

𝐙𝐞𝐧𝐠 💜 profil fotoğrafı
𝐙𝐞𝐧𝐠 💜10 ay önce

Impressive!

SpAItial AI profil fotoğrafı
SpAItial AI10 ay önce

Thanks!

Jakub Červený profil fotoğrafı
Jakub Červený10 ay önce

Cool model, but you need a better viewer:

RThomas 🚀🇿🇦🏁 profil fotoğrafı
RThomas 🚀🇿🇦🏁10 ay önce

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

Arseni Santashev profil fotoğrafı
Arseni Santashev10 ay önce

API soon?

Ai Machete profil fotoğrafı
Ai Machete10 ay önce

Incredible! 👀

Anders Hjemdahl profil fotoğrafı
Anders Hjemdahl10 ay önce

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)?

Hey profil fotoğrafı
Hey10 ay önce

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?

kano profil fotoğrafı
kano7 ay önce

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

Hey profil fotoğrafı
Hey10 ay önce

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

おさ profil fotoğrafı
おさ10 ay önce

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.

PS profil fotoğrafı
PS10 ay önce

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

Jikku Jose profil fotoğrafı
Jikku Jose10 ay önce

Real time? On what hardware?

Mr. Hackathon profil fotoğrafı
Mr. Hackathon10 ay önce

Waitlit?

17 years of song a day profil fotoğrafı
17 years of song a day10 ay önce

please let me in!

Vadim profil fotoğrafı
Vadim10 ay önce

That’s a lot of EM dashes 😂

Hyperstack profil fotoğrafı
Hyperstack10 ay önce

Impressive!

Joe Williams profil fotoğrafı
Joe Williams10 ay önce

That’s insane. I love it 😍

Benzer Videolar

New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!
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New open-source 3D world-generation model. I'm rendering a couple of worlds in the video, so check it out. You'll find the GitHub and the Hugging Face links to the model below. This is a multi-modal world model that you can use for a bunch of things: • To generate new worlds • To reconstruct worlds • To simulate 3D interactive worlds from a prompt, images, or a video You can edit the 3D outputs in Unity and Unreal Engine (they export as meshes, 3DGS files, and point clouds). You can also generate 3D characters in the world and walk around. Pretty fun stuff!

Santiago

65,446 görüntüleme • 6 ay önce

📢WorldAgents: 3D worlds only from 2D image models - without any training! We propose an agentic approach with a Director (VLM) to plan the scene, a Generator (Flux or NanoBanana) for new views, and a Verifier (VLM) for selection / 3D consistency. -> High-fidelity 3D worlds from a single text prompt. What's remarkable: our agents find consistent views from 2D image models to obtain 3D-consistent worlds; this shows that image models contain world priors - agents just need to find them! Great work by Ziya Erkoç Angela Dai
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📢WorldAgents: 3D worlds only from 2D image models - without any training! We propose an agentic approach with a Director (VLM) to plan the scene, a Generator (Flux or NanoBanana) for new views, and a Verifier (VLM) for selection / 3D consistency. -> High-fidelity 3D worlds from a single text prompt. What's remarkable: our agents find consistent views from 2D image models to obtain 3D-consistent worlds; this shows that image models contain world priors - agents just need to find them! Great work by Ziya Erkoç Angela Dai

Matthias Niessner

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Before AI can understand the physical world, it needs data about the physical world. Not just images. Spatial environments. Real streets, buildings, parks, stores, museums, and public spaces captured in 3D. OVER has already reconstructed 290,000+ real-world 3D scenes, creating a growing dataset for the next generation of spatial and embodied AI. And this is exactly the kind of real-world spatial data AI companies are looking for. We're already in conversations with multiple AI and Robotics companies exploring how OVER's dataset can support the development and training of models that need to understand the physical world. The next AI race will be about real-world data.
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Before AI can understand the physical world, it needs data about the physical world. Not just images. Spatial environments. Real streets, buildings, parks, stores, museums, and public spaces captured in 3D. OVER has already reconstructed 290,000+ real-world 3D scenes, creating a growing dataset for the next generation of spatial and embodied AI. And this is exactly the kind of real-world spatial data AI companies are looking for. We're already in conversations with multiple AI and Robotics companies exploring how OVER's dataset can support the development and training of models that need to understand the physical world. The next AI race will be about real-world data.

Over the Reality 🌐

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In our research lab, we are building “real-time dreaming” - the ability to generate fully playable video worlds prompted from any text or image. Our real-time, action conditioned world model (currently running internally at 16fps at 832x480p) is trained on a combination of data, including proprietary Roblox 3D avatar/world interaction data. World models are different from multiplayer engines in that they store state and memory in video latents. Roblox is multiplayer, and we are actively researching optimal ways to simultaneously store state for thousands of players, and keep them in sync with their environment. Our world model leverages database technology which stores all user interactions on Roblox in a vector format that can be used to re-render video and interaction from any camera angle. We see several immediate uses for our Roblox world model. We will use it side-by-side text, image and video prompts as a way to launch auto-generation of immersive worlds. In Roblox Studio, a creator could walk around and use prompts to “paint” a world and then convert it into a 3D representation or direct to Roblox native as a way for many people to play simultaneously. All of this comes alive as we explore the notion of a “Dream Theater” - where one user is dreaming, while others watch and prompt them. 2/4
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In our research lab, we are building “real-time dreaming” - the ability to generate fully playable video worlds prompted from any text or image. Our real-time, action conditioned world model (currently running internally at 16fps at 832x480p) is trained on a combination of data, including proprietary Roblox 3D avatar/world interaction data. World models are different from multiplayer engines in that they store state and memory in video latents. Roblox is multiplayer, and we are actively researching optimal ways to simultaneously store state for thousands of players, and keep them in sync with their environment. Our world model leverages database technology which stores all user interactions on Roblox in a vector format that can be used to re-render video and interaction from any camera angle. We see several immediate uses for our Roblox world model. We will use it side-by-side text, image and video prompts as a way to launch auto-generation of immersive worlds. In Roblox Studio, a creator could walk around and use prompts to “paint” a world and then convert it into a 3D representation or direct to Roblox native as a way for many people to play simultaneously. All of this comes alive as we explore the notion of a “Dream Theater” - where one user is dreaming, while others watch and prompt them. 2/4

Roblox

4,417,163 görüntüleme • 8 ay önce

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:
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📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

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66,918 görüntüleme • 1 yıl önce

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Introducing WorldView 3D, a first-of-its-kind satellite tasking product that delivers up-to-date 3D ground truth anywhere on Earth. From GPS-denied autonomy and intelligence analysis to consumer-grade mapping and disaster response, the world's most important missions increasingly depend on accurate, up-to-date 3D terrain. But for years, keeping 3D maps current at global scale hasn't been possible. WorldView 3D changes that. Built on Vantor’s trusted spatial foundation, our new tasking capability empowers customers to task and receive updated 3D data within 24 hours of image collection. For missions that require greater fidelity, they can also produce high-definition 3D maps anywhere in the world. 3D is the future of spatial intelligence, and Vantor is leading the change. Our trusted spatial foundation already includes 100 million sq km of the world mapped inhighly accurate 3D. Now customers can update the locations that matter most with the speed, scale, and fidelity required for critical missions. Read the full announcement:

Vantor

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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.
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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images.

AK

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1:22

America. Europe. Africa. Asia. Oceania. More than 250,000 real-world locations across 120+ countries have already been transformed into ultra high quality 3D Gaussian Splats by Over the Reality. From city centers and museums to beaches, mountains, and historical landmarks, every mapping created by our community contributes to a growing machine-readable representation of the physical world. This scene started as a simple Insta360 X5 recording and was transformed into a digital twin, where geometry, textures, and spatial structure are preserved and can be used by AI, Robotics, VPS, XR, and next-generation world models. This is what city-scale 3D mapping looks like. The link to become an Insta360 X5 mapper is in the first comment. 👇

Over the Reality 🌐

19,448 görüntüleme • 4 ay önce

📢📢 𝐏𝐞𝐫𝐜𝐇𝐞𝐚𝐝: 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐝 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐢𝐧𝐠𝐥𝐞-𝐈𝐦𝐚𝐠𝐞 𝟑𝐃 𝐇𝐞𝐚𝐝 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐝𝐢𝐭𝐢𝐧𝐠📢📢 PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text. At its core is a generalized 3D head decoder trained with perceptual supervision from DINOv2 and SAM 2.1. We find that our new perceptual loss formulation improves reconstruction fidelity compared to commonly-used methods such as LPIPS. Our trained reconstruction model is able to generate 3D-consistent heads from a single input image. Even with challenging side-view inputs, the model robustly infers missing regions for a coherent, high-fidelity output. In addition, our architecture seamlessly adapts to downstream tasks: by swapping the encoder, we can transform the model into a disentangled 3D editing pipeline. In this scenario, we can control geometry through - potentially hand-drawn - segmentation maps, and condition style via image or text prompt. We also provide an interactive GUI to enable the exploration of our editing pipeline. 🌍 📽️ Great work by Antonio Oroz and Tobias Kirschstein
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📢📢 𝐏𝐞𝐫𝐜𝐇𝐞𝐚𝐝: 𝐏𝐞𝐫𝐜𝐞𝐩𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐝 𝐌𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐒𝐢𝐧𝐠𝐥𝐞-𝐈𝐦𝐚𝐠𝐞 𝟑𝐃 𝐇𝐞𝐚𝐝 𝐑𝐞𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐝𝐢𝐭𝐢𝐧𝐠📢📢 PercHead reconstructs realistic 3D heads from a single image and enables disentangled 3D editing via geometric controls and style inputs from images or text. At its core is a generalized 3D head decoder trained with perceptual supervision from DINOv2 and SAM 2.1. We find that our new perceptual loss formulation improves reconstruction fidelity compared to commonly-used methods such as LPIPS. Our trained reconstruction model is able to generate 3D-consistent heads from a single input image. Even with challenging side-view inputs, the model robustly infers missing regions for a coherent, high-fidelity output. In addition, our architecture seamlessly adapts to downstream tasks: by swapping the encoder, we can transform the model into a disentangled 3D editing pipeline. In this scenario, we can control geometry through - potentially hand-drawn - segmentation maps, and condition style via image or text prompt. We also provide an interactive GUI to enable the exploration of our editing pipeline. 🌍 📽️ Great work by Antonio Oroz and Tobias Kirschstein

Matthias Niessner

18,903 görüntüleme • 11 ay önce

Large-scale Gaussian splats have reached a new level of realism. This is a well-known temple in Bangkok, reconstructed as a high-fidelity 3D environment from 360 captures. At this level, the boundary between video and 3D starts to disappear. But what you’re looking at is not a video. It’s a dense spatial representation of a real place, where geometry, texture, and structure are preserved and made machine-readable. This kind of 3D data can power Visual AI, Robotics navigation, VPS localization, XR experiences, world models, and next-generation spatial computing systems. Built with Over the Reality.
2:46

Large-scale Gaussian splats have reached a new level of realism. This is a well-known temple in Bangkok, reconstructed as a high-fidelity 3D environment from 360 captures. At this level, the boundary between video and 3D starts to disappear. But what you’re looking at is not a video. It’s a dense spatial representation of a real place, where geometry, texture, and structure are preserved and made machine-readable. This kind of 3D data can power Visual AI, Robotics navigation, VPS localization, XR experiences, world models, and next-generation spatial computing systems. Built with Over the Reality.

Over the Reality 🌐

347,424 görüntüleme • 5 ay önce

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

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

AK

62,768 görüntüleme • 3 yıl önce

Welcome to the 3D Layer of the Internet We are thrilled to share our latest features Prompt-to-3D engine and Scene Studio Your text based prompts and photos can now be transformed from simple concepts into fully-rendered, interactive 3D models. It parses complex topology, handles multi-view reconstruction, and even lets you restyle your model instantly with single-click filters like low-poly, voxel, and Voronoi shells. Within the all-new Scene Studio, you can construct immersive 3D environments that reflect your exact style. Position your assets in a full sandbox space, adjust advanced lighting and environment colors, and add the finishing touches with custom camera angles, fog settings, and real-time script parameters. This is the future of the open web. This is
3:00

Welcome to the 3D Layer of the Internet We are thrilled to share our latest features Prompt-to-3D engine and Scene Studio Your text based prompts and photos can now be transformed from simple concepts into fully-rendered, interactive 3D models. It parses complex topology, handles multi-view reconstruction, and even lets you restyle your model instantly with single-click filters like low-poly, voxel, and Voronoi shells. Within the all-new Scene Studio, you can construct immersive 3D environments that reflect your exact style. Position your assets in a full sandbox space, adjust advanced lighting and environment colors, and add the finishing touches with custom camera angles, fog settings, and real-time script parameters. This is the future of the open web. This is

three.ws

37,492 görüntüleme • 4 ay önce

Physical AI for defense and intelligence starts with an accurate model of the world – high-fidelity, geometrically grounded, built for the environments where it actually matters. Today, Niantic Spatial 🌎 is awardable on the Department of War 🇺🇸’s Tradewinds Solutions Marketplace for 3D Reconstruction, our second capability to clear that bar after Visual Positioning, announced earlier this month. Together they deliver two critical layers of the spatial stack: high-fidelity 3D models of contested environments, and precise location within them; a foundation for mission-critical operations that need to visualize and navigate the physical world. The capability is ready. The procurement path is open.
0:24

Physical AI for defense and intelligence starts with an accurate model of the world – high-fidelity, geometrically grounded, built for the environments where it actually matters. Today, Niantic Spatial 🌎 is awardable on the Department of War 🇺🇸’s Tradewinds Solutions Marketplace for 3D Reconstruction, our second capability to clear that bar after Visual Positioning, announced earlier this month. Together they deliver two critical layers of the spatial stack: high-fidelity 3D models of contested environments, and precise location within them; a foundation for mission-critical operations that need to visualize and navigate the physical world. The capability is ready. The procurement path is open.

asim ᯅ

14,288 görüntüleme • 4 ay önce

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:
1:20

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 görüntüleme • 1 yıl önce

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.
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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 görüntüleme • 26 gün önce

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.
2:32

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 görüntüleme • 3 yıl önce

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.
0:56

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 görüntüleme • 2 yıl önce

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
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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 görüntüleme • 2 ay önce

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.
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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 görüntüleme • 8 ay önce

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
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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 görüntüleme • 25 gün önce