Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Hyper3D just launched WorldGen, and it takes 3D generation from single assets to full scenes. From one image, it builds a structured 3D scene where every important object is its own editable asset. You can move it, replace it, edit it, and interact with it. How it works: 1.... Upload a scene image 2. WorldGen identifies the objects 3. Generate the full scene with Max Gen and walk around with WASD 4. Select any object and move, delete, replace, or regenerate it with Hyper3D Rodin 5. Click "Interactive" to see physical properties 6. Export to Blender, Unity, or UE It pairs Rodin's independent Mesh assets with 3D Gaussian Splatting for the environment, and predicts position, scale, collision, mass, and friction. Built for embodied AI training, film pre-viz with video models like Seedance 2.5, game dev, and XR. From DeemosTech, whose CAST research won a Best Paper Award at SIGGRAPH 2025. Try Hyper3D WorldGen: Get updates Hyper3D by Deemos: 14-day Free Trial Code: 35TLO1MS #Hyper3D #DeemosTech #Rodin #WorldGen #AI3Dshow more

LX™

18,819 subscribers

91,794 görüntüleme • 2 gün önce •via X (Twitter)

Oyun Bilim & Teknoloji

Anya Rossi• Live Now

Private livecam show

21 Yorum

🥹🌻 profil fotoğrafı

🥹🌻2 gün önce

3D just got smarter

Hitilafu profil fotoğrafı

Hitilafu2 gün önce

Thanks for the support! Try it here:

João_KE profil fotoğrafı

João_KE2 gün önce

Not just a static view. The output is a structured scene you can edit, simulate, and interact with.

. profil fotoğrafı

.2 gün önce

This looks amazing

agy lizy profil fotoğrafı

agy lizy2 gün önce

This is the future

Sheriee🌟 profil fotoğrafı

Sheriee🌟2 gün önce

Rodin handles the individual assets, so each one has complete geometry.

Himself profil fotoğrafı

Himself2 gün önce

The editing is fire

Singapore Premium Tickets 🇸🇬 profil fotoğrafı

Singapore Premium Tickets 🇸🇬2 gün önce

For film, it works like a 3D director's stage. Set the scene, camera, and blocking, then hand it to a video model like Seedance 2.5.

🖤Its_Linnet❤️Lin💜 profil fotoğrafı

🖤Its_Linnet❤️Lin💜2 gün önce

For embodied AI, it generates SimReady assets from a single real world photo, and you can quickly vary layout, lighting, and obstacles.

Poppy profil fotoğrafı

Poppy2 gün önce

Game dev just got easier

Mwangi profil fotoğrafı

Mwangi2 gün önce

The team behind it, DeemosTech, won a Best Paper Award at SIGGRAPH 2025 for CAST.

z4 profil fotoğrafı

z42 gün önce

It also works for XR, like turning one image into a space you can explore on devices like Apple Vision Pro.

Tweeds profil fotoğrafı

Tweeds2 gün önce

Very impressive tech

ZACK™️💎 profil fotoğrafı

ZACK™️💎2 gün önce

Turning one image into a full 3D scene is impressive

*Tycoon* profil fotoğrafı

*Tycoon*2 gün önce

Hyper3D is cooking

s.loyne profil fotoğrafı

s.loyne2 gün önce

One image to a world

IGOR profil fotoğrafı

IGOR2 gün önce

Huge potential here

Mwangi profil fotoğrafı

Mwangi2 gün önce

AI 3D is moving fast

Venasio 💫 profil fotoğrafı

Venasio 💫2 gün önce

It starts from a single image. Indoor, street view, natural view, and complex scenes all work as input.

New Dawn profil fotoğrafı

New Dawn2 gün önce

Only three commercial companies got that award in 2025: Google, Meta, and DeemosTech.

darkhumour profil fotoğrafı

darkhumour2 gün önce

For game dev, it cuts the time spent on initial modeling and scene assembly.

Benzer Videolar

GPT-6 Astra can already plan, code, and build. The more interesting question is what happens when it can call a dedicated 3D tool at the exact moment a project needs an asset. So we connected Hyper3D Rodin MCP and asked Astra to build an interactive Solar System Explorer. Astra handled the app logic and integration. When it needed 3D models, it called Hyper3D Rodin directly. Hyper3D Rodin generated the required assets, and Astra brought them back into the project and continued building from there. That is where the workflow gets useful. Instead of stopping to search for assets or creating them manually, Astra can keep moving while Hyper3D handles the specialized 3D generation, with more control over the output and assets that are easier to integrate into the final project. The result is a browser-based solar system where the generated 3D models are part of the actual experience, not just outputs sitting in a model viewer.

GPT-6 Astra can already plan, code, and build. The more interesting question is what happens when it can call a dedicated 3D tool at the exact moment a project needs an asset. So we connected Hyper3D Rodin MCP and asked Astra to build an interactive Solar System Explorer. Astra handled the app logic and integration. When it needed 3D models, it called Hyper3D Rodin directly. Hyper3D Rodin generated the required assets, and Astra brought them back into the project and continued building from there. That is where the workflow gets useful. Instead of stopping to search for assets or creating them manually, Astra can keep moving while Hyper3D handles the specialized 3D generation, with more control over the output and assets that are easier to integrate into the final project. The result is a browser-based solar system where the generated 3D models are part of the actual experience, not just outputs sitting in a model viewer.

GitHub Projects Community

11,097 görüntüleme • 20 gün önce

🔥 img2threejs × GPT-6 Astra × Hyper3D Live showcase: This is probably one of the most commercially valuable workflows I’ve tested so far. Image → Hyper3D → img2threejs → GPT-6 Astra → Interactive Three.js experience. 1/ Hyper3D handles the 3D reconstruction. 2/ img2threejs turns that result into a structured workflow that an AI coding model can actually work with. 3/ And GPT-6 Astra follows the img2threejs pipeline - turning the reconstructed asset into a structured, editable, and interactive Three.js experience with animation, VFX, lighting, and application logic. The important part isn’t just generating a 3D model, It’s what happens after the model exists. Instead of ending with a static asset, you can continue building around it with code: → Games → Interactive product experiences → 3D websites → Character showcases → Virtual worlds → Rapid prototypes 3D generation becomes the beginning of the workflow - not the final output. And when you combine high-quality reconstruction with an AI model capable of understanding and building the surrounding experience, the path from image → 3D → usable product becomes dramatically shorter. This is exactly the direction I want to push img2threejs. More experiments with OpenAI GPT-6 Astra × Hyper3D by Deemos are coming this month. 👨‍🍳

🔥 img2threejs × GPT-6 Astra × Hyper3D Live showcase: This is probably one of the most commercially valuable workflows I’ve tested so far. Image → Hyper3D → img2threejs → GPT-6 Astra → Interactive Three.js experience. 1/ Hyper3D handles the 3D reconstruction. 2/ img2threejs turns that result into a structured workflow that an AI coding model can actually work with. 3/ And GPT-6 Astra follows the img2threejs pipeline - turning the reconstructed asset into a structured, editable, and interactive Three.js experience with animation, VFX, lighting, and application logic. The important part isn’t just generating a 3D model, It’s what happens after the model exists. Instead of ending with a static asset, you can continue building around it with code: → Games → Interactive product experiences → 3D websites → Character showcases → Virtual worlds → Rapid prototypes 3D generation becomes the beginning of the workflow - not the final output. And when you combine high-quality reconstruction with an AI model capable of understanding and building the surrounding experience, the path from image → 3D → usable product becomes dramatically shorter. This is exactly the direction I want to push img2threejs. More experiments with OpenAI GPT-6 Astra × Hyper3D by Deemos are coming this month. 👨‍🍳

Nick

12,239 görüntüleme • 24 gün önce

How about one image straight to an editable UE5 or Blender scene? Lumera just dropped paper - Engine-Native Editable 3D World Reconstruction with Objects & Lighting. You give it an image. It figures out what's in the scene, rebuilds every object as a separate mesh, then recreates the lighting as actual engine lights you can move and tweak. Even pulls an HDR environment out of the shot. The lighting part is the big deal here. Every other image-to-3D approach bakes it in. Lumera keeps it a parameter, like the scene was built in the engine from the start. Code and weights marked as "Soon". Paper -

How about one image straight to an editable UE5 or Blender scene? Lumera just dropped paper - Engine-Native Editable 3D World Reconstruction with Objects & Lighting. You give it an image. It figures out what's in the scene, rebuilds every object as a separate mesh, then recreates the lighting as actual engine lights you can move and tweak. Even pulls an HDR environment out of the shot. The lighting part is the big deal here. Every other image-to-3D approach bakes it in. Lumera keeps it a parameter, like the scene was built in the engine from the start. Code and weights marked as "Soon". Paper -

Stefan 3D AI

47,223 görüntüleme • 1 ay önce

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

✨ I made a little demo of #vibejam sponsor Tripo and how you can use it to generate assets for your games In this case I'm generating an apartment flat that's half destroyed and open for my drone sim to fly around, take cover and shoot at enemies from First I write the prompt and select Nano Banana to generate an image first, I get a few different ones, and I select the one I like Then that one is converted into a 3d object called a Smart Mesh Smart Mesh is different than regular image-to-3d models because usually you get issues with uneven polygon distribution, too dense geometry and poor edge flow, so while you think you save time with generating it with AI you end up doing lots of editing after to fix the topology which kinda defeats the point Smart Mesh doesn't have any of this, it's fast and clean low-poly models you can drop into your game Anyway, after generating the model I can then see it in 3d, and if I don't like it, regenerate it, or if I like it start texturing it Once that's done, I export it as a GLB, FBX or OBJ and drop it in (usually I use GLB as ThreeJS seems to prefer that), in my case I just drop the URL into Cursor and let it figure out the placement of the asset into my game!

@levelsio

72,338 görüntüleme • 5 ay önce

I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

I'm excited to share the Geo Register Plugin for LichtFeld Studio from the LichtFeld community! This plugin helps bring Gaussian splat scenes into real-world geographic space. It registers a scene to WGS-84 and ECEF coordinates, so you can click any point on the model and get its latitude, longitude and altitude. It supports multiple georeferencing sources, including EXIF GPS data, image position CSVs, RealityScan camera parameters and saved similarity transforms. Once the scene is registered, you can export geo-referenced splat models as LAS, LAZ or 3D Tiles datasets for use in GIS and 3D mapping workflows. Built for anyone working with drone data, photogrammetry, Gaussian splatting, GIS, ArcGIS or CesiumJS. Link in the comment below!

MrNeRF

51,983 görüntüleme • 4 ay önce

Today, we’re launching a new way to create with AI. With OpenArt Worlds, you can generate a fully navigable 3D environment from a single prompt or image, step inside it, and capture shots exactly the way you envision them. No more starting over. No more inconsistent scenes. You build the world once - and create inside it. • Move through your scene freely • Find your angles • Add characters and elements • Capture production-ready shots

Today, we’re launching a new way to create with AI. With OpenArt Worlds, you can generate a fully navigable 3D environment from a single prompt or image, step inside it, and capture shots exactly the way you envision them. No more starting over. No more inconsistent scenes. You build the world once - and create inside it. • Move through your scene freely • Find your angles • Add characters and elements • Capture production-ready shots

OpenArt

7,303,718 görüntüleme • 6 ay önce

Astra built this three.js game with gorgeous graphics, then recreated every scene in Blender and made the trailer too. It feels like a massive upgrade from Sol for 3D graphics and front-end design. The UI feels way more polished and less like AI slop. The rocks, trees, monuments, and characters are all procedural code, keeping the game under 2 MB before the image and music assets. The level of autonomy is unbelievable. It needed very little steering, though I still had to keep pushing it to work harder on the details. The UI icons, buttons, and ornaments were made as transparent PNGs with GPT Image 2.

Astra built this three.js game with gorgeous graphics, then recreated every scene in Blender and made the trailer too. It feels like a massive upgrade from Sol for 3D graphics and front-end design. The UI feels way more polished and less like AI slop. The rocks, trees, monuments, and characters are all procedural code, keeping the game under 2 MB before the image and music assets. The level of autonomy is unbelievable. It needed very little steering, though I still had to keep pushing it to work harder on the details. The UI icons, buttons, and ornaments were made as transparent PNGs with GPT Image 2.

Meng To

137,306 görüntüleme • 26 gün önce

I tried to see how far I could push my current AI + 3D workflow and build a playable 3D game prototype in just 3 days😶 Not by asking AI to make the whole game in one prompt. I had my own idea, knew what I wanted to build, and used AI as a tool: - for concepting, - asset generation & texturing, - gameplay logic with the new UE 5.8 MCP + Claude Code. And honestly, it is crazy how far you can get now with the right workflow. Full video - #UE5 #ClaudeCode #AI3D #3DAI

I tried to see how far I could push my current AI + 3D workflow and build a playable 3D game prototype in just 3 days😶 Not by asking AI to make the whole game in one prompt. I had my own idea, knew what I wanted to build, and used AI as a tool: - for concepting, - asset generation & texturing, - gameplay logic with the new UE 5.8 MCP + Claude Code. And honestly, it is crazy how far you can get now with the right workflow. Full video - #UE5 #ClaudeCode #AI3D #3DAI

Stefan 3D AI

21,406 görüntüleme • 3 ay önce

With Atlas, you can literally make films. And it also basically solves first-frame-last-frame transitions. Reconstruct a 3D scene from images, then position the camera exactly where you want it, and let Atlas fill in what the camera would see along that path. You’re in the director's chair, staging the scene. Here we made a 1 min video with a couple images of totally different scenes, and we set where the camera keyframes should move through each part of the scene. The scene-to-scene transitions are great. Atlas makes it feel like a smooth shot with no cuts.

With Atlas, you can literally make films. And it also basically solves first-frame-last-frame transitions. Reconstruct a 3D scene from images, then position the camera exactly where you want it, and let Atlas fill in what the camera would see along that path. You’re in the director's chair, staging the scene. Here we made a 1 min video with a couple images of totally different scenes, and we set where the camera keyframes should move through each part of the scene. The scene-to-scene transitions are great. Atlas makes it feel like a smooth shot with no cuts.

David Pantera

27,529 görüntüleme • 28 gün önce

Another city. Another full 3D Gaussian Splat reconstruction. This entire neighborhood was captured while walking with an Insta360 X5, then processed with our GPU pipeline and transformed into a high-fidelity, machine-readable 3D environment with Over the Reality. What you’re seeing is not an isolated virtual world. It’s a 3D digital twin of a real-world location, built from structured spatial data where every building, street, and detail can become usable by AI, robotics, and XR. And it’s happening at scale, across cities in more than 180 countries every day. Have an Insta360 X5 and want to become a mapper? Apply here: Want to build a 3D Gaussian Splat from a video you already recorded? Do it for free at powered by Over the Reality.

Another city. Another full 3D Gaussian Splat reconstruction. This entire neighborhood was captured while walking with an Insta360 X5, then processed with our GPU pipeline and transformed into a high-fidelity, machine-readable 3D environment with Over the Reality. What you’re seeing is not an isolated virtual world. It’s a 3D digital twin of a real-world location, built from structured spatial data where every building, street, and detail can become usable by AI, robotics, and XR. And it’s happening at scale, across cities in more than 180 countries every day. Have an Insta360 X5 and want to become a mapper? Apply here: Want to build a 3D Gaussian Splat from a video you already recorded? Do it for free at powered by Over the Reality.

Over the Reality 🌐

400,282 görüntüleme • 5 ay önce

This is your reminder to play The Dream, 404’s Chroma Award-winning game built using SN17-generated assets. At 404, we aim to help kickstart the next revolution in gaming around AI-native games. That starts with democratising 3D content creation through Bittensor, making it easier for creators to build virtual worlds, games, and AR/VR/XR experiences without needing massive production teams. The long-term vision is to let creators describe the game or world they want to build, then generate the assets, sound, voice, and environment around it. The Dream is an amazing example of that vision. It is a fully playable 3D open-world experience created by a two-person team in just 30 days, using over 12,000 assets generated with 404. The goal is not to replace enterprise game development. It is to create a new layer where individuals and small teams can build and explore high-fidelity game worlds without needing a massive AAA-style pipeline. Sometimes, you need to see it to believe it. Play The Dream yourself at:

This is your reminder to play The Dream, 404’s Chroma Award-winning game built using SN17-generated assets. At 404, we aim to help kickstart the next revolution in gaming around AI-native games. That starts with democratising 3D content creation through Bittensor, making it easier for creators to build virtual worlds, games, and AR/VR/XR experiences without needing massive production teams. The long-term vision is to let creators describe the game or world they want to build, then generate the assets, sound, voice, and environment around it. The Dream is an amazing example of that vision. It is a fully playable 3D open-world experience created by a two-person team in just 30 days, using over 12,000 assets generated with 404. The goal is not to replace enterprise game development. It is to create a new layer where individuals and small teams can build and explore high-fidelity game worlds without needing a massive AAA-style pipeline. Sometimes, you need to see it to believe it. Play The Dream yourself at:

404

19,367 görüntüleme • 2 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.

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

This is the latest Google research on XR Interaction: World Mouse. Instead of using hand rays or gestures to point at things in the physical space, you move a cursor with a mouse and the system figures out where that cursor should sit in the 3D world. It works in two main ways: - On an object, the cursor follows the surface, so you can move precisely across walls, screens, furniture, or virtual objects. - Between objects, the system creates a smooth bridge through space, so the cursor can travel from one surface to another. That means you can go from a 2D panel to a 3D object, place content on a real wall, manipulate virtual objects, or interact with physical devices through digital proxies. This is the type of news, tools and product updates I share weekly in my newsletter. If you are into 3D XR and AI make sure to check it out (link on top of my profile). Based on “World Mouse: Exploring Interactions with a Cross-Reality Cursor” by Esen K. Tütüncü, Mar Gonzalez-Franco, Khushman Patel, and Eric J. Gonzalez

Gabriele Romagnoli

29,032 görüntüleme • 6 ay önce

I think a lot of people are missing what GPT-6 can really do because they're using it out of the box. Give it the right free MCPs and skills, and it starts to feel like magic. Start with Blender MCP. In my tests, it works faster and uses fewer tokens than computer use. If you want to have fun with procedural modeling, go for it. For more detailed models, I use 3D generators and let GPT handle the processing, rigging and animation. You don't need to burn tokens making it build every detail from scratch. I've spent the last six months putting this setup together. These are the tools I actually use for Blender, Unity, Godot and Unreal, including my 3D generation tools. Full breakdown:

I think a lot of people are missing what GPT-6 can really do because they're using it out of the box. Give it the right free MCPs and skills, and it starts to feel like magic. Start with Blender MCP. In my tests, it works faster and uses fewer tokens than computer use. If you want to have fun with procedural modeling, go for it. For more detailed models, I use 3D generators and let GPT handle the processing, rigging and animation. You don't need to burn tokens making it build every detail from scratch. I've spent the last six months putting this setup together. These are the tools I actually use for Blender, Unity, Godot and Unreal, including my 3D generation tools. Full breakdown:

Stefan 3D AI

26,513 görüntüleme • 21 gün önce

The battle of the Text/Image to 3D Model 👍- To make this fair, I tested the same image across all platforms and used the first generated result. No cherry picking allowed! I also tested what I think are the 4 top services to use right now. Let me know what you think is the best 3D mesh generation from these platforms however I think it's quite obvious that Tencent Hy 2.5 is currently the king 👑 It wins in overall detail and sharpness, accuracy in relation to the input image, as well as texture generation and projection. Tripo 2.5 comes in second and Rodin 3rd with Trellis in 4th place. However, Rodin has the best topology since it can generate a mesh in quads BUT I left all of them in triangles to keep it fair. Hi3DGen can't be counted as it can't even generate textures and it has no multi-image support. Meshy was another option but the results were not great. Is there any other AI 3D Gen services that you know of that you want me to test as well?

The battle of the Text/Image to 3D Model 👍- To make this fair, I tested the same image across all platforms and used the first generated result. No cherry picking allowed! I also tested what I think are the 4 top services to use right now. Let me know what you think is the best 3D mesh generation from these platforms however I think it's quite obvious that Tencent Hy 2.5 is currently the king 👑 It wins in overall detail and sharpness, accuracy in relation to the input image, as well as texture generation and projection. Tripo 2.5 comes in second and Rodin 3rd with Trellis in 4th place. However, Rodin has the best topology since it can generate a mesh in quads BUT I left all of them in triangles to keep it fair. Hi3DGen can't be counted as it can't even generate textures and it has no multi-image support. Meshy was another option but the results were not great. Is there any other AI 3D Gen services that you know of that you want me to test as well?

Travis Davids

61,232 görüntüleme • 1 yıl önce

#PixVerseWorldModel is doing something pretty interesting with AI video. One of the things that stood out to me is the WASD experience. AI video usually follows a simple workflow: write a prompt, generate a clip, and watch the result. With PixVerse World Model, you can actually move around inside the generated world, control the camera, and use prompts to influence the environment, characters, and actions as you move through the scene. The interesting part is how movement and prompting work together. Instead of generating a completely new clip every time you want something to change, you can keep interacting with the world and see how it responds. Text prompts start to feel less like instructions for generating a video and more like another way of interacting with the world itself. It makes PixVerse World Model feel less like traditional AI video and more like a playable, generated environment. Maybe the more interesting question for AI video isn’t just “what can it generate?” It’s “what can we actually do once we’re inside it?” Try now:

#PixVerseWorldModel is doing something pretty interesting with AI video. One of the things that stood out to me is the WASD experience. AI video usually follows a simple workflow: write a prompt, generate a clip, and watch the result. With PixVerse World Model, you can actually move around inside the generated world, control the camera, and use prompts to influence the environment, characters, and actions as you move through the scene. The interesting part is how movement and prompting work together. Instead of generating a completely new clip every time you want something to change, you can keep interacting with the world and see how it responds. Text prompts start to feel less like instructions for generating a video and more like another way of interacting with the world itself. It makes PixVerse World Model feel less like traditional AI video and more like a playable, generated environment. Maybe the more interesting question for AI video isn’t just “what can it generate?” It’s “what can we actually do once we’re inside it?” Try now:

Nayeem Sheikh

14,959 görüntüleme • 8 gün önce

I'm not a 3d modeler. I barely know how to open blender let alone do anything functional in it. But with the siddharth ahuja blender MCP and Claude I was able to vibe code an entire environment for my upcoming grow a garden game in Portals Claude used the MCP to assemble and arrange every single asset in this space (i literally don't even know how to import an asset into blender lol) within the spawn of a few hours and with access to a folder of low poly assets. When I had it start doing its own modeling, it even matched the style of the environment to keep things consistent If you are building UGC games and you don't know anything about modeling or design like me, you gotta try it out...

I'm not a 3d modeler. I barely know how to open blender let alone do anything functional in it. But with the siddharth ahuja blender MCP and Claude I was able to vibe code an entire environment for my upcoming grow a garden game in Portals Claude used the MCP to assemble and arrange every single asset in this space (i literally don't even know how to import an asset into blender lol) within the spawn of a few hours and with access to a folder of low poly assets. When I had it start doing its own modeling, it even matched the style of the environment to keep things consistent If you are building UGC games and you don't know anything about modeling or design like me, you gotta try it out...

Hugh MFER (PORTALS Guy)

180,963 görüntüleme • 8 ay önce

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

WOW. 😳 Apple just quietly won the 3D maps war at WWDC. Gaussian Splatting is coming to Apple Maps Flyover this fall. Apple Maps Flyover covers 300+ cities. Until yesterday, every single one was built on standard drone photogrammetry. The technology captures photos from the air and reconstructs 3D geometry from them. Gaussian Splatting does not reconstruct geometry. It represents the scene as millions of tiny 3D ellipsoids, each one carrying its own color and opacity information based on how light actually behaves in that location. The output is not a mesh model. It is a field of light. When you move through it, it does not crumble at the edges. The detail holds because it was never geometry to begin with. Apple has been hiring for this for years. Their SHARP model, published in research last year, generates photorealistic 3D scenes from a single image in under a second. Google has more sensor data than anyone. More Street View cars, more satellites, more capture history. On navigation accuracy and geodata depth, Google Maps is still ahead by most measures. But fidelity in 3D city rendering is a different competition, and Apple just set a bar in that. Most people will experience this in the fall without knowing the name of the technology. They will open Flyover, look at a city they know, and notice it looks different. Real, not rendered. That is the moment Gaussian Splatting stops being a research term and becomes something a billion people use. Bookmark this. It will look prescient by October.

Shruti

19,832 görüntüleme • 3 ay önce