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InSpatio-WorldFM. transforms single photos into multi-view consistent 3D worlds. - Explicit 3D anchors + implicit neural state. - Zero-drift spatial reasoning. Real-time interactive exploration.

Wildminder

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10,808 просмотров • 4 месяцев назад •via X (Twitter)

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We’re open-sourcing HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive *3D worlds* from text, images, and videos. Outputs can be integrated into game engines and embodied simulation pipelines. Key highlights: 🔹 One-click world generation Turn text or image into interactive 3D worlds automatically. 🔹 Pipeline-ready 3D outputs Editable 3D worlds for Unity and Unreal Engine, with standard 3D exports including mesh, 3DGS, and point clouds. 🔹 Unified world model system One model family for world generation and reconstruction across synthetic and real-world scenes. 🔹 Interactive character mode Explore generated 3D worlds in real time with physics-aware movement and collision support. ✨ Apply for access: 🔗 GitHub: 🤗 Hugging Face: 📄 Technical Report:

We’re open-sourcing HY-World 2.0, a multimodal world model that generates, reconstructs, and simulates interactive 3D worlds from text, images, and videos. Outputs can be integrated into game engines and embodied simulation pipelines. Key highlights: 🔹 One-click world generation Turn text or image into interactive 3D worlds automatically. 🔹 Pipeline-ready 3D outputs Editable 3D worlds for Unity and Unreal Engine, with standard 3D exports including mesh, 3DGS, and point clouds. 🔹 Unified world model system One model family for world generation and reconstruction across synthetic and real-world scenes. 🔹 Interactive character mode Explore generated 3D worlds in real time with physics-aware movement and collision support. ✨ Apply for access: 🔗 GitHub: 🤗 Hugging Face: 📄 Technical Report:

Tencent Hy

371,287 просмотров • 3 месяцев назад

RTFM (Real-Time Frame Model) explores real-time, interactive frame generation from images, producing new views on the fly without explicit 3D. It differs from Marble, which targets high-fidelity persistent worlds (3DGS). We’re interested in how ideas from both might connect 🌎

RTFM (Real-Time Frame Model) explores real-time, interactive frame generation from images, producing new views on the fly without explicit 3D. It differs from Marble, which targets high-fidelity persistent worlds (3DGS). We’re interested in how ideas from both might connect 🌎

World Labs

74,499 просмотров • 5 месяцев назад

Spatial reconstruction is a long-context problem: real scenes come with hundreds of images. But O(N²) transformer-based models don’t scale efficiently. Introducing: 🤐ZipMap (CVPR ’26): Linear-Time, Stateful 3D Reconstruction via Test-Time Training (TTT). ZipMap “zips” a large image collection into an implicit TTT scene state in a single linear-time operation. The state will then be decoded into spatial outputs, and can be queried efficiently for novel-view geometry and appearance (~100 FPS) ZipMap is not only much faster (>20× faster than VGGT), but also matches or surpasses the accuracy of all SOTA models.

Spatial reconstruction is a long-context problem: real scenes come with hundreds of images. But O(N²) transformer-based models don’t scale efficiently. Introducing: 🤐ZipMap (CVPR ’26): Linear-Time, Stateful 3D Reconstruction via Test-Time Training (TTT). ZipMap “zips” a large image collection into an implicit TTT scene state in a single linear-time operation. The state will then be decoded into spatial outputs, and can be queried efficiently for novel-view geometry and appearance (~100 FPS) ZipMap is not only much faster (>20× faster than VGGT), but also matches or surpasses the accuracy of all SOTA models.

Haian Jin

79,033 просмотров • 5 месяцев назад

🚀 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:

🚀 Announcing Echo — our new frontier model for 3D world generation. Echo turns a simple text prompt or image into a fully explorable, 3D-consistent world. Instead of disconnected views, the result is a single, coherent spatial representation you can move through freely. This is part of a bigger shift in AI: from generating pixels and tokens to generating spaces. Echo predicts a geometry-grounded 3D scene at metric scale, meaning every novel view, depth map, and interaction comes from the same underlying world — not independent hallucinations. Once generated, the world is interactive in real time. You control the camera, explore from any angle, and render instantly — even on low-end hardware, directly in the browser. High-quality 3D world exploration is no longer gated by expensive equipment. Under the hood, Echo infers a physically grounded 3D representation and converts it into a renderable format. For our web demo, we use 3D Gaussian Splatting (3DGS) for fast, GPU-friendly rendering — but the representation itself is flexible and can be easily adapted. Why this matters: consistent 3D worlds unlock real workflows — digital twins, 3D design, game environments, robotics simulation, and more. From a single photo or a line of text, Echo builds worlds that are reliable, editable, and spatially faithful. Echo also enables scene editing and restyling. Change materials, remove or add objects, explore design variations — all while preserving global 3D consistency. Editing no longer breaks the world. This is only the beginning. Echo is the foundation for future world models with dynamics, physical reasoning, and richer interaction — environments that don’t just look right, but behave right. Explore the generated worlds on our website and sign up for the closed beta. The era of spatial intelligence starts here. 🌍 #Echo #WorldModels #SpatialAI #3DFoundationModels Check it out:

SpAItial AI

176,105 просмотров • 7 месяцев назад

Introducing RTFM (Real-Time Frame Model): a highly efficient World Model that generates video frames in real time as you interact with it, powered by a single H100 GPU. RTFM renders persistent and 3D consistent worlds, both real and imaginary. Try our demo of RTFM today!

Introducing RTFM (Real-Time Frame Model): a highly efficient World Model that generates video frames in real time as you interact with it, powered by a single H100 GPU. RTFM renders persistent and 3D consistent worlds, both real and imaginary. Try our demo of RTFM today!

World Labs

340,567 просмотров • 9 месяцев назад

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:

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

168,530 просмотров • 9 месяцев назад

📢 OneCanvas: 3D Scene Understanding via Panoramic Reprojection We extract features from video frames and reproject them into one occlusion-free view of the whole scene that a 2D VLM reads just like a normal image. We can center this view on any viewpoint, including an agent's own pose for situated reasoning. The same projection lets us create spatial training tasks with no human annotation, solvable only by reasoning over the 3D positions of real object features placed on an otherwise empty canvas. The result is a stock 2D VLM that reasons in 3D, setting a new state of the art across spatial benchmarks at far less compute. 🌐 ▶️ Great work by Bartłomiej Baranowski & Dave Zhenyu Chen

📢 OneCanvas: 3D Scene Understanding via Panoramic Reprojection We extract features from video frames and reproject them into one occlusion-free view of the whole scene that a 2D VLM reads just like a normal image. We can center this view on any viewpoint, including an agent's own pose for situated reasoning. The same projection lets us create spatial training tasks with no human annotation, solvable only by reasoning over the 3D positions of real object features placed on an otherwise empty canvas. The result is a stock 2D VLM that reasons in 3D, setting a new state of the art across spatial benchmarks at far less compute. 🌐 ▶️ Great work by Bartłomiej Baranowski & Dave Zhenyu Chen

Matthias Niessner

25,153 просмотров • 1 месяц назад

"near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence [...] a Neural Object Field that is learned concurrently with a pose graph optimization process in order to robustly accumulate information into a consistent 3D representation"

"near real-time method for 6-DoF tracking of an unknown object from a monocular RGBD video sequence [...] a Neural Object Field that is learned concurrently with a pose graph optimization process in order to robustly accumulate information into a consistent 3D representation"

Fabien Benetou

222,914 просмотров • 3 лет назад

🚨 Google releases its most advanced world model Genie 3. It lets you build fully interactive 3D worlds you can explore in real-time with a text prompt, or even upload your own image, and turning it into your own playable world.

🚨 Google releases its most advanced world model Genie 3. It lets you build fully interactive 3D worlds you can explore in real-time with a text prompt, or even upload your own image, and turning it into your own playable world.

Alvaro Cintas

23,013 просмотров • 6 месяцев назад

Mind blown by this new CVPR 2026 paper: tttLRM🤯 It introduces a Test-Time Training layer for long-context autoregressive 3D reconstruction with linear complexity. Multiple image observations are compressed into fast weights that keep updating during inference, forming an implicit scene representation that can be decoded into explicit formats such as 3D Gaussian Splats. Progressive 3D reconstruction from streaming inputs is getting wild🥳It’s interesting to see how AI inference and real-world capture are starting to converge.

Mind blown by this new CVPR 2026 paper: tttLRM🤯 It introduces a Test-Time Training layer for long-context autoregressive 3D reconstruction with linear complexity. Multiple image observations are compressed into fast weights that keep updating during inference, forming an implicit scene representation that can be decoded into explicit formats such as 3D Gaussian Splats. Progressive 3D reconstruction from streaming inputs is getting wild🥳It’s interesting to see how AI inference and real-world capture are starting to converge.

KIRI Engine - 3D Scanner App

16,949 просмотров • 4 месяцев назад

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

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

37,254 просмотров • 1 месяц назад

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

3D-LLM: Injecting the 3D World into Large Language Models paper page: Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. Powerful as these models can be, they are not grounded in the 3D physical world, which involves richer concepts such as spatial relationships, affordances, physics, layout, and so on. In this work, we propose to inject the 3D world into large language models and introduce a whole new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on. Using three types of prompting mechanisms that we design, we are able to collect over 300k 3D-language data covering these tasks. To efficiently train 3D-LLMs, we first utilize a 3D feature extractor that obtains 3D features from rendered multi- view images. Then, we use 2D VLMs as our backbones to train our 3D-LLMs. By introducing a 3D localization mechanism, 3D-LLMs can better capture 3D spatial information. Experiments on ScanQA show that our model outperforms state-of-the-art baselines by a large margin (e.g., the BLEU-1 score surpasses state-of-the-art score by 9%). Furthermore, experiments on our held-in datasets for 3D captioning, task composition, and 3D-assisted dialogue show that our model outperforms 2D VLMs. Qualitative examples also show that our model could perform more tasks beyond the scope of existing LLMs and VLMs.

AK

249,708 просмотров • 3 лет назад

BIG NEWS: Introducing SDF-Splats! 🚨 I just solved one of the biggest problems in 3D Gaussian Splatting: GAPS & HOLES! 🕳️❌ My hybrid approach combines: ✨ Explicit Gaussian Splats (for detail) ✨ Implicit SDFs (for continuity) The result? Seamless 3D reconstruction with NO visible holes, NO transparency artifacts, and REAL-TIME performance at 30-60 FPS! 🔥 Traditional Gaussian Splatting looks amazing but breaks apart in under-sampled regions. SDF-Splats fills every gap using on-demand raymarching through signed distance fields—zero preprocessing, zero neural nets, pure GPU magic! ⚡ Read the full research paper: #GaussianSplatting #SDF Radiance Fields MrNeRF #RealTime3D #VR #3DReconstruction #AIResearch

BIG NEWS: Introducing SDF-Splats! 🚨 I just solved one of the biggest problems in 3D Gaussian Splatting: GAPS & HOLES! 🕳️❌ My hybrid approach combines: ✨ Explicit Gaussian Splats (for detail) ✨ Implicit SDFs (for continuity) The result? Seamless 3D reconstruction with NO visible holes, NO transparency artifacts, and REAL-TIME performance at 30-60 FPS! 🔥 Traditional Gaussian Splatting looks amazing but breaks apart in under-sampled regions. SDF-Splats fills every gap using on-demand raymarching through signed distance fields—zero preprocessing, zero neural nets, pure GPU magic! ⚡ Read the full research paper: #GaussianSplatting #SDF Radiance Fields MrNeRF #RealTime3D #VR #3DReconstruction #AIResearch

Daniel Skaale

21,031 просмотров • 8 месяцев назад

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!

📢 Intrinsic Image Fusion for Multi-View 3D Material Reconstruction 📢 We combine generative material priors with inverse path tracing: 1) define a parametric texture space 2) fuse monocular predictions across views into consistent textures 3) optimize low-dimensional parameters for physically-grounded reconstructions. The results are relightable PBR textures for 3D scenes: check out the result on a real-world 3D scan from the ScanNet++ dataset! 🌍 🎥 Great work by Peter Kocsis Lukas Höllein!

Matthias Niessner

23,087 просмотров • 7 месяцев назад

You as agent: super cool Gemini demo by Filip Havlena👤🍎🖥️ We've been experimenting with agentic interfaces at Creative Lab and this one was so fun: he turned himself into a 3d agent with spatial awareness + reasoning loop + tools (docs/emails with Google Workspace CLI). Made in Three.js. Real-time screencap ▶️

You as agent: super cool Gemini demo by Filip Havlena👤🍎🖥️ We've been experimenting with agentic interfaces at Creative Lab and this one was so fun: he turned himself into a 3d agent with spatial awareness + reasoning loop + tools (docs/emails with Google Workspace CLI). Made in Three.js. Real-time screencap ▶️

Alexander Chen

39,564 просмотров • 4 месяцев назад

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.

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

305,663 просмотров • 3 лет назад

We are looking at the future of VR, YouTube & Google Street View. This is zip-NeRF, a 3D neural rendering tech rapidly approaching the quality of a real, high-res drone flight video. Think of NeRF as transporting reality into simulation. Metaverse will finally work this time.

We are looking at the future of VR, YouTube & Google Street View. This is zip-NeRF, a 3D neural rendering tech rapidly approaching the quality of a real, high-res drone flight video. Think of NeRF as transporting reality into simulation. Metaverse will finally work this time.

Jim Fan

1,260,433 просмотров • 3 лет назад

Claude Fable 5 has impressive spatial reasoning capabilities that are immediately relevant for engineering and design: In this example I gave Claude Fable 5 a single photo of a hierarchical mesh torus (image generated using a text-to-image model); and in one shot, no iteration, it built a full interactive 3D simulation: ~1,400 nodes & 4,400 fibers, realistic mass–spring physics you can grab, compress, twist… plus strain-based sonification so you can 'hear' the structure vibrate. It inferred the complete 3D topology from a partial 2D view. ⤵️

Claude Fable 5 has impressive spatial reasoning capabilities that are immediately relevant for engineering and design: In this example I gave Claude Fable 5 a single photo of a hierarchical mesh torus (image generated using a text-to-image model); and in one shot, no iteration, it built a full interactive 3D simulation: ~1,400 nodes & 4,400 fibers, realistic mass–spring physics you can grab, compress, twist… plus strain-based sonification so you can 'hear' the structure vibrate. It inferred the complete 3D topology from a partial 2D view. ⤵️

Markus J. Buehler

32,003 просмотров • 1 месяц назад

Escape your tiny screens and step into a 500" world with the XREAL 1S ✅ REAL 3D™: The world’s first on‑glasses 3D spatial technology that transforms all your 2D games, movies, and photos into an immersive 3D experience ✅ Your 500" cinematic escape: 1200p HD resolution, 52° FOV, and 120 Hz refresh for a sharp, smooth, big‑screen experience anywhere ✅ All Your Favourite Features from the One Series: Native 3DoF spatial anchoring, electrochromic dimming, and seamless USB‑C plug‑and‑play, all powered by the X1 chip ✅ Cobalt Blue Finish: Radiating bold coolness and timeless presence Launching at just $449. Tap the link here to order your XREAL 1S today. 🚀 #XREAL #1S #ARGlasses #LaunchDay #FutureTech

Escape your tiny screens and step into a 500" world with the XREAL 1S ✅ REAL 3D™: The world’s first on‑glasses 3D spatial technology that transforms all your 2D games, movies, and photos into an immersive 3D experience ✅ Your 500" cinematic escape: 1200p HD resolution, 52° FOV, and 120 Hz refresh for a sharp, smooth, big‑screen experience anywhere ✅ All Your Favourite Features from the One Series: Native 3DoF spatial anchoring, electrochromic dimming, and seamless USB‑C plug‑and‑play, all powered by the X1 chip ✅ Cobalt Blue Finish: Radiating bold coolness and timeless presence Launching at just $449. Tap the link here to order your XREAL 1S today. 🚀 #XREAL #1S #ARGlasses #LaunchDay #FutureTech

XREAL 👓

137,504 просмотров • 7 месяцев назад

This is SO crazy - The new "Spatial Scenes" feature turns your photos into frozen moments in time — making Vision Pro feel more like a real-life time machine than ever before. You can quite literally step into these photos, since they’re not just 3D versions but actually fully spatial. That means you can now look behind objects and change your perspective. If this isn’t pure sci-fi or magic come to life, I don’t know what is. I can’t stop smiling — we’re living in the craziest of all timelines.

This is SO crazy - The new "Spatial Scenes" feature turns your photos into frozen moments in time — making Vision Pro feel more like a real-life time machine than ever before. You can quite literally step into these photos, since they’re not just 3D versions but actually fully spatial. That means you can now look behind objects and change your perspective. If this isn’t pure sci-fi or magic come to life, I don’t know what is. I can’t stop smiling — we’re living in the craziest of all timelines.

Phil Traut ᯅ

89,065 просмотров • 1 год назад