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Ever tried 3D scanning a printed circuit board (PCB)? Capturing tiny solder joints, dense routing, and reflective surfaces is no easy task. In this demo, the AutoScan Inspec 2 takes on a highly complex electronic component to show what high-accuracy 3D metrology can achieve. Watch how it captures fine...

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Wevolver

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36,150 görüntüleme • 4 ay önce •via X (Twitter)

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Meet Tencent HY 3D Studio 1.2 👋 With this major upgrade to our 3D creation pipeline, you can generate assets with sculpt-level detail and fine-grained interactive control. Starting today, the studio is officially open for Public Beta — no application required. Tencent HY 3D Studio 1.2 introduces a suite of features built for creative precision, including: 🧩 PartGen 1.5: High-Precision Component Partitioning — 1536³ Resolution: Boosted from 1024³ for crystal-clear model splitting and ultra-fine detail retention. — Fine-Grained Interaction: Introducing intuitive, brush-based control for precise, manual component editing. — Shape Adherence: Drastically improved geometry integrity for even the most intricate objects. 🎨 Tencent HY 3D 3.1: Sculpt-Level Detail — Enhanced Geometry: Fine-grained details that adapt perfectly to stylized inputs.. — Texture Fidelity: A massive leap in color accuracy and texture fidelity, reflecting your original input with stunning realism. — 8-View Control: Expanded from 4 to 8 input views for ultimate reconstruction accuracy. ✨We’re so excited to see what you create! Try it now:
1:02

Meet Tencent HY 3D Studio 1.2 👋 With this major upgrade to our 3D creation pipeline, you can generate assets with sculpt-level detail and fine-grained interactive control. Starting today, the studio is officially open for Public Beta — no application required. Tencent HY 3D Studio 1.2 introduces a suite of features built for creative precision, including: 🧩 PartGen 1.5: High-Precision Component Partitioning — 1536³ Resolution: Boosted from 1024³ for crystal-clear model splitting and ultra-fine detail retention. — Fine-Grained Interaction: Introducing intuitive, brush-based control for precise, manual component editing. — Shape Adherence: Drastically improved geometry integrity for even the most intricate objects. 🎨 Tencent HY 3D 3.1: Sculpt-Level Detail — Enhanced Geometry: Fine-grained details that adapt perfectly to stylized inputs.. — Texture Fidelity: A massive leap in color accuracy and texture fidelity, reflecting your original input with stunning realism. — 8-View Control: Expanded from 4 to 8 input views for ultimate reconstruction accuracy. ✨We’re so excited to see what you create! Try it now:

Tencent Hy

83,314 görüntüleme • 8 ay önce

Su-57's Augmented Reality (AR) is in production; KnAAZ plant developed an AR-system to overlay the a/c's 3D CAD data directly onto real felon during assembly inspection. >this system was tested on the landing-gear bay, which allowed workers to compare the actual installation of assemblies with the digital model >in fact, Holo-Lens+tablet were used for positioning and inspection, with the digital assembly data loaded from the plant's engineering database. >It was developed to make marking, visual inspection and assembly verification faster and reduce human errors. Source: 2020 KnAAZ presentation on AR applications in a/c assembly (🧵)
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Su-57's Augmented Reality (AR) is in production; KnAAZ plant developed an AR-system to overlay the a/c's 3D CAD data directly onto real felon during assembly inspection. >this system was tested on the landing-gear bay, which allowed workers to compare the actual installation of assemblies with the digital model >in fact, Holo-Lens+tablet were used for positioning and inspection, with the digital assembly data loaded from the plant's engineering database. >It was developed to make marking, visual inspection and assembly verification faster and reduce human errors. Source: 2020 KnAAZ presentation on AR applications in a/c assembly (🧵)

VERTEX

14,440 görüntüleme • 16 gün önce

I've been a proponent of the idea of combining 3d and generative ai for a long time - as a way to get the best of both worlds: The precision and control of 3d - the detail, realism, lighting and speed of generative models. Here's another example of that idea, realized through a procedural system - entirely vibe-coded with Fable. Inspired by a photograph I took of an old tower in a town in southern France. In the first part of the video I'm using Krea AI's realtime model - later examples show how Nano Banana 2 can be used to take 3d stills and transform them to pretty much anything. What do you think of workflows like this compared to purely generating video or images from text, image or video input?
0:34

I've been a proponent of the idea of combining 3d and generative ai for a long time - as a way to get the best of both worlds: The precision and control of 3d - the detail, realism, lighting and speed of generative models. Here's another example of that idea, realized through a procedural system - entirely vibe-coded with Fable. Inspired by a photograph I took of an old tower in a town in southern France. In the first part of the video I'm using Krea AI's realtime model - later examples show how Nano Banana 2 can be used to take 3d stills and transform them to pretty much anything. What do you think of workflows like this compared to purely generating video or images from text, image or video input?

Martin Nebelong

20,265 görüntüleme • 1 ay önce

🚀 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:
1:07

🚀 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

177,073 görüntüleme • 9 ay önce

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding
1:01

THIS GUY VIBE CODED A TRUE 3D PROPERTY TOUR TOOL WITH CLAUDE CODE not the fake 360 photo stitching that matterport does, actual 3D right now if you're a real estate agent and want a property tour you need: > a matterport camera ($3000+) > a photographer who knows how to use it > matterport's monthly subscription > and you still get a glorified panorama, not real 3D this guy built a tool that takes your existing captures (camera files, drone shots, 360 photos, EVEN phone captures) and turns them into a real interactive 3D tour in one workflow no specialized hardware, no photographer, no switching between 5 different tools real estate agents, property managers, airbnb hosts, architects, interior designers all of them are paying hundreds per property for something this tool does from captures they already have the painful part was never understanding the end result. everyone knows what a 3D tour looks like. the pain is going from a pile of messy capture files to something usable without it turning into a technical project this solves that with one workflow you dump your files in and get a 3D tour out insane what you can do with vibe coding

Om Patel

158,397 görüntüleme • 4 ay önce

"#AI will never create clean meshes" AI 👇 This is the new #AI model from NVIDIA called Meshtron (jeez what a name 😅). It generates meshes of complex 3D objects closely resembling those created by professional artists. It opens the door to a more realistic generation of detailed 3D assets for animation, gaming, and virtual environments. The current version accepts the following control inputs: - Point cloud: Determines the shape of the output mesh. - Face count: Determines the density of the output mesh. - Quad ratio: Switches between quad and triangle tessellation. - Creativity: Can be adjusted to generate extra details in the model. Meshtron can either serve as a stand-alone remesher to improve the quality of existing models, or be used in tandem with a text-to-3D or image-to-3D model to generate artist-grade meshes from scratch.
0:26

"#AI will never create clean meshes" AI 👇 This is the new #AI model from NVIDIA called Meshtron (jeez what a name 😅). It generates meshes of complex 3D objects closely resembling those created by professional artists. It opens the door to a more realistic generation of detailed 3D assets for animation, gaming, and virtual environments. The current version accepts the following control inputs: - Point cloud: Determines the shape of the output mesh. - Face count: Determines the density of the output mesh. - Quad ratio: Switches between quad and triangle tessellation. - Creativity: Can be adjusted to generate extra details in the model. Meshtron can either serve as a stand-alone remesher to improve the quality of existing models, or be used in tandem with a text-to-3D or image-to-3D model to generate artist-grade meshes from scratch.

Gabriele Romagnoli

2,177,587 görüntüleme • 1 yıl önce

These stainless-steel beads form a powder that is over six times finer than flour, meaning each particle is too small to see with the naked eye. Powders like this are the basic building material of a type of metal 3D printing called "laser powder bed fusion" or (PBF-LB). It works by spreading out a thin layer of metal powder and using a powerful laser to melt the powder together into a specific shape. Then the process continues by laying down a fresh layer of powder on top and repeating. Each layer is leveled off by a wiper. It’s like the way you might level off a cup of flour with the back of a knife. To make a good 3D-printed part, the powder must be spread in an even, consistent way. Now, thanks to NIST research, we can watch the powder spreading and study its behavior. This work will improve the consistency and quality of metal 3D printed parts. Video Credit: National Institute of Standards and Technology #engineering #technology #3dprinting #additivemanufacturing
1:15

These stainless-steel beads form a powder that is over six times finer than flour, meaning each particle is too small to see with the naked eye. Powders like this are the basic building material of a type of metal 3D printing called "laser powder bed fusion" or (PBF-LB). It works by spreading out a thin layer of metal powder and using a powerful laser to melt the powder together into a specific shape. Then the process continues by laying down a fresh layer of powder on top and repeating. Each layer is leveled off by a wiper. It’s like the way you might level off a cup of flour with the back of a knife. To make a good 3D-printed part, the powder must be spread in an even, consistent way. Now, thanks to NIST research, we can watch the powder spreading and study its behavior. This work will improve the consistency and quality of metal 3D printed parts. Video Credit: National Institute of Standards and Technology #engineering #technology #3dprinting #additivemanufacturing

Wevolver

73,297 görüntüleme • 4 ay 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. 👨‍🍳
2:34

🔥 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 • 19 gün önce

🚨 Electrical engineers are going to hate this. Someone just turned React into a circuit board factory. Write code. Get a real PCB manufactured and delivered to your door. It's called tscircuit. React for Electronics. No Altium. No $10,000/year licenses. No 6-month learning curve. You write React components. But instead of and , you write , , and . The same way you build a website. That's how you build a circuit board now. Here's what this thing actually does: → Design real circuit boards using TypeScript and React → Edit code in your IDE, watch the circuit update in real time → Auto-generates schematics, PCB layouts, and 3D previews from your code → Automatic part selection and bill of materials generation → Built-in autorouting algorithm for PCB traces → Export to Gerber files and send directly to a manufacturer → Online playground. Design circuits in your browser right now. Here's the wildest part: The creator wrote 40 lines of TypeScript. From that he got a full PCB, a schematic, and a 3D preview. Then he exported it, sent it to a manufacturer, and got a real working circuit board delivered. 40 lines of code. A real physical product. This is free. Write React. Get hardware. $20K+ in bounties already paid to contributors. 8 years in the making. 100% Open Source. MIT License.
3:42

🚨 Electrical engineers are going to hate this. Someone just turned React into a circuit board factory. Write code. Get a real PCB manufactured and delivered to your door. It's called tscircuit. React for Electronics. No Altium. No $10,000/year licenses. No 6-month learning curve. You write React components. But instead of and , you write , , and . The same way you build a website. That's how you build a circuit board now. Here's what this thing actually does: → Design real circuit boards using TypeScript and React → Edit code in your IDE, watch the circuit update in real time → Auto-generates schematics, PCB layouts, and 3D previews from your code → Automatic part selection and bill of materials generation → Built-in autorouting algorithm for PCB traces → Export to Gerber files and send directly to a manufacturer → Online playground. Design circuits in your browser right now. Here's the wildest part: The creator wrote 40 lines of TypeScript. From that he got a full PCB, a schematic, and a 3D preview. Then he exported it, sent it to a manufacturer, and got a real working circuit board delivered. 40 lines of code. A real physical product. This is free. Write React. Get hardware. $20K+ in bounties already paid to contributors. 8 years in the making. 100% Open Source. MIT License.

Nav Toor

381,280 görüntüleme • 6 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.
0:24

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

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!
0:30

Want to create an avatar from a single image? FlexAvatar is a transformer model that creates full 360°, high-quality, and expressive 3D head avatar from just a single portrait image in minutes. Real-time Demo: FlexAvatar's lightweight architecture allows both animation and rendering in real-time, enabling interactive user experiences. To create a new 3D head avatar, only one image is required, e.g., from a webcam. The final avatar is ready after 2 minutes. Architecture: Under the hood, FlexAvatar adopts a transformer-based encoder-decoder design. The encoder maps the input image onto a latent avatar space, while the decoder produces 3D Gaussian attribute maps by incorporating the animation signal via cross-attention. The model learns all facial animations directly from the data without relying on pre-built 3D face models. This equips the avatars with realistic facial expressions. The internal avatar latent space can be conveniently used to integrate additional observations of a person via fitting. This enables use-cases where more than one image of a person is available, e.g., from a phone scan of the person. We train jointly on 2D monocular videos and multi-view data. However, in monocular videos, the animation signal leaks the target viewpoint, causing the model to produce incomplete 3D heads. We call this phenomenon entanglement of driving signal and target viewpoint. To prevent entanglement, we introduce bias sinks. These are learnable tokens that indicate whether a training sample stems from a monocular or a multi-view dataset. During training, the model learns to produce incomplete 3D heads only when the monocular token is present. During inference, FlexAvatar then always uses the multi-view token for which the model has learned to produce complete 3D heads. This simple design allows to combine the generalizability from monocular data with the quality of multi-view data. FlexAvatar summary: - Input: Single-image, phone scan, or monocular video - Output: Full 360° head avatar - Expressive animations - Real-time rendering and animation - Generalization to any portrait - Create a new avatar in 2 minutes - Use bias sinks to combine 2D and 3D data 🏠 🌍 🎥 Great work by Tobias Kirschstein and Simon Giebenhain!

Matthias Niessner

96,371 görüntüleme • 9 ay önce

Enter the SPLATRIX! We digitized Henry Pearce and converted him over to 4DGS to be played back in real-time. 3D Gaussians Splats in Motion. 4DGS. Our goal was to improve the quality of our AeonX & 4DGS pipeline, with automated background removal to retain fine hair details as well as to improve overall surface consistency on a full torso capture. We're currently using a post process to temporally filter the 2D frames. We still need to solve 4DGS temporal stability. We're hoping to test with Jonathon Luiten dynamic gaussian code soon... 🙏 for that script! We captured this performance under global illumination. This is an easy task compared to RGB+W lighting, which is going to be much harder! This is our next challenge. We're posting these snippet videos to show our progress with 3D/4DGS. We plan to write a full blog post on our 3D/4DGS journey soon. 3D Gaussian Splatting #GaussianSplatting #inria #radiancefields #ir #aeonx #rgbw #idatronic #ximea #sibr #hdri #60fps More info here - Higher Quality Videos:
1:14

Enter the SPLATRIX! We digitized Henry Pearce and converted him over to 4DGS to be played back in real-time. 3D Gaussians Splats in Motion. 4DGS. Our goal was to improve the quality of our AeonX & 4DGS pipeline, with automated background removal to retain fine hair details as well as to improve overall surface consistency on a full torso capture. We're currently using a post process to temporally filter the 2D frames. We still need to solve 4DGS temporal stability. We're hoping to test with Jonathon Luiten dynamic gaussian code soon... 🙏 for that script! We captured this performance under global illumination. This is an easy task compared to RGB+W lighting, which is going to be much harder! This is our next challenge. We're posting these snippet videos to show our progress with 3D/4DGS. We plan to write a full blog post on our 3D/4DGS journey soon. 3D Gaussian Splatting #GaussianSplatting #inria #radiancefields #ir #aeonx #rgbw #idatronic #ximea #sibr #hdri #60fps More info here - Higher Quality Videos:

Infinite-Realities

90,662 görüntüleme • 2 yıl önce

📢📢 𝐀𝐯𝐚𝐭𝟑𝐫 📢📢 Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito
0:38

📢📢 𝐀𝐯𝐚𝐭𝟑𝐫 📢📢 Avat3r creates high-quality 3D head avatars from just a few input images in a single forward pass with a new dynamic 3DGS reconstruction model. Video: Project: Our core idea is to make Gaussian Reconstruction Models animatable. We find that a simple cross-attention to an expression code sequence is already sufficient to model complex facial expressions. We then incorporate position maps from DUSt3R and feature maps from Sapiens to facilitate the prediction task. While DUSt3R's position maps act as a pixel-aligned initialization for the Gaussians' positions, the Sapiens feature maps help the cross-view transformer to match corresponding image tokens in the 4 input images. One major challenge in creating a 3D head avatar from smartphone images comes from inconsistent facial expressions when the subject could not remain perfectly static during the capture. We eliminate this static requirement by simply showing our model input images with different facial expressions during training. This technique makes our model robust to inconsistent input images later on. Finally, we show that despite the model has been trained with 4 input images, one can even create a 3D head avatar when only a single image is available. To achieve this, we employ a pre-trained 3D GAN to lift the single image to 3D and then render the 4 input images for our model. This allows us to create 3D head avatars from single images and even highly out-of-distribution examples like AI generated faces, paintings or statues. Great work by Tobias Kirschstein from his internship at Meta with Javier Romero, Artem Sevastopolsky, and Shunsuke Saito

Matthias Niessner

74,818 görüntüleme • 1 yıl önce

Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives paper page: Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations.
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Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives paper page: Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations.

AK

38,571 görüntüleme • 3 yıl önce

China just turned the night sky into a masterpiece of precision and intelligence. 🌌 What struck me most about this is how seamlessly innovation turns into art. A drone show in Chongqing just broke the Guinness World Record with 11,787 synchronized drones, creating breathtaking 3D animations that looked closer to CGI than real life. No human pilots. No delays. No crashes. Every movement guided by AI and GPS, choreographed with perfect timing. To me, this is far more than a light show. It’s a glimpse into how technology, creativity, and coordination can merge to shape a new era of expression and innovation. When intelligence takes flight, it doesn’t just illuminate the sky — it redefines what’s possible. Could this be the moment where technology begins to turn the world itself into its stage? #AI #Innovation #Technology #Drones #Automation #Creativity #China #Engineering #FutureOfWork #DigitalArt Credits: longliveai
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China just turned the night sky into a masterpiece of precision and intelligence. 🌌 What struck me most about this is how seamlessly innovation turns into art. A drone show in Chongqing just broke the Guinness World Record with 11,787 synchronized drones, creating breathtaking 3D animations that looked closer to CGI than real life. No human pilots. No delays. No crashes. Every movement guided by AI and GPS, choreographed with perfect timing. To me, this is far more than a light show. It’s a glimpse into how technology, creativity, and coordination can merge to shape a new era of expression and innovation. When intelligence takes flight, it doesn’t just illuminate the sky — it redefines what’s possible. Could this be the moment where technology begins to turn the world itself into its stage? #AI #Innovation #Technology #Drones #Automation #Creativity #China #Engineering #FutureOfWork #DigitalArt Credits: longliveai

Pascal Bornet

2,493,164 görüntüleme • 8 ay önce

The AR GenAI solution available on AR Code SaaS continues to demonstrate how fast immersive AR content can now be created from a single image. In this new demo, a contemporary art statuette is captured with a single photo, then instantly transformed into a fully optimized 3D model ready for augmented reality display. As shown in the video, the generated 3D artwork is automatically published through an AR QR Code, allowing anyone to launch the experience instantly in augmented reality directly from their smartphone. No app required. This example highlights a powerful capability of AR GenAI by AR Code: artworks can now be easily digitized and transformed into interactive AR experiences starting from just one photo. For contemporary art galleries, museums, cultural institutions, artists, and art marketplaces, this opens a new path to quickly create scalable 3D collections and immersive exhibitions. Object Photo → AI 3D generation → AR QR Code → Instant WebAR What previously required complex 3D scanning setups, photogrammetry pipelines, or specialized expertise can now be achieved in minutes. AR GenAI makes it possible to digitize artworks, publish AR experiences, and scale immersive cultural content faster than ever. Learn more: Follow AR Code for updates. #ARGenAI #ARCode #ImageTo3D #ImageToAR #WebAR #AugmentedReality #DigitalArt #Museums #ArtTech
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The AR GenAI solution available on AR Code SaaS continues to demonstrate how fast immersive AR content can now be created from a single image. In this new demo, a contemporary art statuette is captured with a single photo, then instantly transformed into a fully optimized 3D model ready for augmented reality display. As shown in the video, the generated 3D artwork is automatically published through an AR QR Code, allowing anyone to launch the experience instantly in augmented reality directly from their smartphone. No app required. This example highlights a powerful capability of AR GenAI by AR Code: artworks can now be easily digitized and transformed into interactive AR experiences starting from just one photo. For contemporary art galleries, museums, cultural institutions, artists, and art marketplaces, this opens a new path to quickly create scalable 3D collections and immersive exhibitions. Object Photo → AI 3D generation → AR QR Code → Instant WebAR What previously required complex 3D scanning setups, photogrammetry pipelines, or specialized expertise can now be achieved in minutes. AR GenAI makes it possible to digitize artworks, publish AR experiences, and scale immersive cultural content faster than ever. Learn more: Follow AR Code for updates. #ARGenAI #ARCode #ImageTo3D #ImageToAR #WebAR #AugmentedReality #DigitalArt #Museums #ArtTech

Maxime Maisonneuve

19,003 görüntüleme • 6 ay önce

AR GenAI by AR Code is transforming the food industry. Creating an AR experience for a dish can now start with a single photo. As shown in the video, a single dessert photo is converted into an AR-ready 3D model with realistic textures and depth. AR Code SaaS then instantly generates an AR QR Code to share the experience anywhere. Customers can launch it immediately with a quick QR Code scan. No app required. Photo → AI 3D generation → AR QR Code → Instant WebAR What once required specialized software, costly workflows, and advanced 3D skills is now becoming accessible in minutes. This is reshaping how restaurants and food brands create, publish, and distribute immersive content at scale. Learn more: Follow AR Code for updates. #ARGenAI #ARCode #FoodTech #RestaurantTech #ImageTo3D #ImageToAR #GenerativeAI #GenAI #AI3D #AI #3DFromImage #WebAR
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AR GenAI by AR Code is transforming the food industry. Creating an AR experience for a dish can now start with a single photo. As shown in the video, a single dessert photo is converted into an AR-ready 3D model with realistic textures and depth. AR Code SaaS then instantly generates an AR QR Code to share the experience anywhere. Customers can launch it immediately with a quick QR Code scan. No app required. Photo → AI 3D generation → AR QR Code → Instant WebAR What once required specialized software, costly workflows, and advanced 3D skills is now becoming accessible in minutes. This is reshaping how restaurants and food brands create, publish, and distribute immersive content at scale. Learn more: Follow AR Code for updates. #ARGenAI #ARCode #FoodTech #RestaurantTech #ImageTo3D #ImageToAR #GenerativeAI #GenAI #AI3D #AI #3DFromImage #WebAR

Maxime Maisonneuve

21,170 görüntüleme • 8 ay önce