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A visualization of a flow model that transforms a 1D standard Gaussian into a Gaussian mixture. The heatmap shows the changing density and the white lines reflect the trajectories of individual samples over time.

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Alec Helbling

10,726 subscribers

29,004 görüntüleme • 5 ay önce •via X (Twitter)

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I am releasing the “MLSharp” app for macOS. MLSharp is a premium macOS application that transforms 2D images into immersive 3D Gaussian Splatting (3DGS) models using state-of-the-art AI.
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I am releasing the “MLSharp” app for macOS. MLSharp is a premium macOS application that transforms 2D images into immersive 3D Gaussian Splatting (3DGS) models using state-of-the-art AI.

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A drone scan of the partial glacier collapse in Blatten, reconstructed using 3D Gaussian Splatting. New approaches to disaster visualization can provide a rapid overview of the situation.
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A drone scan of the partial glacier collapse in Blatten, reconstructed using 3D Gaussian Splatting. New approaches to disaster visualization can provide a rapid overview of the situation.

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CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting CLM trains a 102-million Gaussian model on the MatrixCity BigCity Aerial Dataset under 4 hours using a single RTX 4090, achieving a PSNR of 25.
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CLM: Removing the GPU Memory Barrier for 3D Gaussian Splatting CLM trains a 102-million Gaussian model on the MatrixCity BigCity Aerial Dataset under 4 hours using a single RTX 4090, achieving a PSNR of 25.

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#3D model using Gaussian splatting with approx. 1400 photos of a drone flight over the Kastel-Staadt hermitage near #Trier. I had already calculated a photogrammetry model last year, the details of the plants in this model are fantastic.
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#3D model using Gaussian splatting with approx. 1400 photos of a drone flight over the Kastel-Staadt hermitage near #Trier. I had already calculated a photogrammetry model last year, the details of the plants in this model are fantastic.

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Big news I found a way to convert Gaussian Splatting into a standard mesh while preserving all the visual advantages of Gaussian Splatting, such as reflections, shadows, and lighting that changes depending on the viewing angle. The result is excellent performance, even on an old laptop or a VR headset. And of course, the mesh can be animated like any regular 3D mesh, while still keeping the visual benefits of GS. I’ll soon show a full-body character animated using this technique.
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Big news I found a way to convert Gaussian Splatting into a standard mesh while preserving all the visual advantages of Gaussian Splatting, such as reflections, shadows, and lighting that changes depending on the viewing angle. The result is excellent performance, even on an old laptop or a VR headset. And of course, the mesh can be animated like any regular 3D mesh, while still keeping the visual benefits of GS. I’ll soon show a full-body character animated using this technique.

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In the Orbit of Solaris – A gaussian splat reconstruction of a scene from Tarkovsky’s masterpiece
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The Gaussian integral is a key concept in mathematics, often used in probability, statistics, and physics. It calculates the area under the bell-shaped curve of the Gaussian (or normal) distribution. ✍️
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The Gaussian integral is a key concept in mathematics, often used in probability, statistics, and physics. It calculates the area under the bell-shaped curve of the Gaussian (or normal) distribution. ✍️

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📢 I’m testing ViggleAI new AI tool, PINOC, and it’s seriously impressive. You can create 3D Gaussian splatting models from just one image and animate them with a video! It’s the first AI I’ve tried that can animate Gaussian splatting models. I also talked to someone from Viggle, and they might add the option to import your own Gaussian splatting models and animate them. If that happens, I’ll probably make a video tutorial showing how to boost the detail level of a Gaussian splatting model by 4x to create highly realistic animated characters.
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The Gaussian integral is a key concept in mathematics, often used in probability, statistics, and physics. It calculates the area under the bell-shaped curve of the Gaussian (or normal) distribution. 📹math.animations
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The Gaussian integral is a key concept in mathematics, often used in probability, statistics, and physics. It calculates the area under the bell-shaped curve of the Gaussian (or normal) distribution. 📹math.animations

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The Shield (2002–2008) pushed television into a new space, centering a cop who keeps crossing lines and following it all the way through, setting a standard that a lot of shows have been chasing since.
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The Shield (2002–2008) pushed television into a new space, centering a cop who keeps crossing lines and following it all the way through, setting a standard that a lot of shows have been chasing since.

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A 3D Gaussian Splat by David "Navi" Romero of the aftermath of a huge fire in Denver. Unlike a standard 2D video, the splat allows us to fly through the wreckage of the largest fire Denver has seen in decades. SuperSplat Link👇
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A 3D Gaussian Splat by David "Navi" Romero of the aftermath of a huge fire in Denver. Unlike a standard 2D video, the splat allows us to fly through the wreckage of the largest fire Denver has seen in decades. SuperSplat Link👇

Will Eastcott

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Gaussian Splatting is pretty wild. Being able to take dense collections of assets and package them into fast, real-time splats opens up a ton of new possibilities for look-dev, previs, and interactive scenes. For this test, I built a pile of 400 skulls - roughly 40+ million polygons (and over 400 8K texture instances) - rendered out a 600 frame dataset on The Render Network with OTOY OctaneRender and then converted the result into a single splat asset. Now it runs in real-time as one compact Gaussian Splat.
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Gaussian Splatting is pretty wild. Being able to take dense collections of assets and package them into fast, real-time splats opens up a ton of new possibilities for look-dev, previs, and interactive scenes. For this test, I built a pile of 400 skulls - roughly 40+ million polygons (and over 400 8K texture instances) - rendered out a 600 frame dataset on The Render Network with OTOY OctaneRender and then converted the result into a single splat asset. Now it runs in real-time as one compact Gaussian Splat.

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Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video Contribution quote from the paper: In summary, our main contributions are • a comprehensive pipeline for reconstructing the shape, appearance, and behavior of real-world garments using Gaussian splatting, • an algorithm for registering garment meshes to multi- view videos with an optimization procedure based on Gaussian splatting, and • a Gaussian Garment representation that combines triangle meshes with Gaussian textures to capture photorealistic appearance and can be used as a fully controllable 3D asset.
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Gaussian Garments: Reconstructing Simulation-Ready Clothing with Photorealistic Appearance from Multi-View Video Contribution quote from the paper: In summary, our main contributions are • a comprehensive pipeline for reconstructing the shape, appearance, and behavior of real-world garments using Gaussian splatting, • an algorithm for registering garment meshes to multi- view videos with an optimization procedure based on Gaussian splatting, and • a Gaussian Garment representation that combines triangle meshes with Gaussian textures to capture photorealistic appearance and can be used as a fully controllable 3D asset.

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Happy to announce the results of our latest research, which takes 3D Gaussian Splatting to the next level: "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets," which has been accepted at #SIGGRAPH2024!🎉 Find it here:
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Happy to announce the results of our latest research, which takes 3D Gaussian Splatting to the next level: "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets," which has been accepted at #SIGGRAPH2024!🎉 Find it here:

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The Ring (Montreal) — Dormant Memories series New alternate scene, and an iteration on the UX and flow. Starting from a volumetric capture of a real location in Montreal, reconstructed as 3D Gaussian Splats, I then used World Labs' Marble to generate alternate versions of that same space. Running in real time using spark. #GaussianSplatting #3DGS #pointcloud #sparkjs #threejs

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GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.
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GS^3: Efficient Relighting with Triple Gaussian Splatting Abstract: We present a spatial and angular Gaussian based representation and a triple splatting process, for real-time, high-quality novel lighting-and-view synthesis from multi-view point-lit input images. To describe complex ap pearance, we employ a Lambertian plus a mixture of angular Gaussians as an effective reflectance function for each spatial Gaussian. To generate self-shadow, we splat all spatial Gaussians towards the light source to obtain shadow values, which are further refined by a small multi-layer perceptron. To compensate for other effects like global illumination, another network is trained to compute and add a per-spatial-Gaussian RGB tuple. The effectiveness of our representation is demonstrated on 30 samples with a wide variation in geometry (from solid to fluffy) and appearance (from translucent to anisotropic), as well as using different forms of input data, including rendered images of synthetic/reconstructed objects, photographs captured with a handheld camera and a flash, or from a professional lightstage. We achieve a training time of 40-70 minutes and a rendering speed of 90 fps on a single commodity GPU. Our results compare favorably with state-of-the-art techniques in terms of quality/performance.

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Watch Gemini 3 code a visualization of plasma flow in a tokamak and write a poem capturing the physics of fusion. ⬇️
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Watch Gemini 3 code a visualization of plasma flow in a tokamak and write a poem capturing the physics of fusion. ⬇️

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