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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... show more

MrNeRF

16,833 subscribers

27,354 Aufrufe • vor 2 Jahren •via X (Twitter)

Wissenschaft & Technologie Bildung Kunst

Anya Rossi• Live Now

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8 Kommentare

Profilbild von MrNeRF

MrNeRFvor 2 Jahren

Paper: Project:

Profilbild von MrNeRF

MrNeRFvor 2 Jahren

Method:

Profilbild von Chris Smith

Chris Smithvor 2 Jahren

Wow

Profilbild von Bryson Jones

Bryson Jonesvor 2 Jahren

This is fkn cool!

Profilbild von MrNeRF

MrNeRFvor 2 Jahren

It is!

Profilbild von No thanks honey

No thanks honeyvor 2 Jahren

Oh my god , you should try this one actually

Profilbild von Hector

Hectorvor 2 Jahren

pipeline to chaos clothing in unreal would be crazy

Profilbild von IUSsshh

IUSsshhvor 2 Jahren

@nezubn looks like this is something relevant that we were talking about

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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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MrNeRF

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More and more users are turning their 360 footage into fully explorable 3D Gaussian Splats with View it here: This reconstruction started from a simple capture and is now a fully explorable 3D Gaussian Splat rendered in the FreeGaussian viewer, complete with point cloud reconstruction and high-fidelity spatial detail. More and more creators are discovering that their existing footage can become immersive 3D scenes. And they’re doing it for free with FreeGaussian

More and more users are turning their 360 footage into fully explorable 3D Gaussian Splats with View it here: This reconstruction started from a simple capture and is now a fully explorable 3D Gaussian Splat rendered in the FreeGaussian viewer, complete with point cloud reconstruction and high-fidelity spatial detail. More and more creators are discovering that their existing footage can become immersive 3D scenes. And they’re doing it for free with FreeGaussian

Over the Reality 🌐

14,845 Aufrufe • vor 5 Monaten

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MrNeRF

34,782 Aufrufe • vor 1 Jahr

I'm testing tree generation using only two reference images. Basically, my Gaussian Splatting generation technique works with almost anything. I can't wait to see the final result once I’ve gathered a huge library of realistic assets for my game. For optimization, I’m planning to use a LOD system that dynamically reduces the number of Gaussian splats based on the distance from the camera.

I'm testing tree generation using only two reference images. Basically, my Gaussian Splatting generation technique works with almost anything. I can't wait to see the final result once I’ve gathered a huge library of realistic assets for my game. For optimization, I’m planning to use a LOD system that dynamically reduces the number of Gaussian splats based on the distance from the camera.

Alex

30,982 Aufrufe • vor 4 Monaten

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🇺🇸 Insta360 Spatial Capture is officially coming to the U.S. on Sep. 28! Update the Insta360 app to the latest version (2.34.0) via your mobile app store or download it from our website, and experience Spatial Capture with Insta360 X4 Air, X5, and X6. Powered by 3D Gaussian Splatting, Spatial Capture lets you scan a room or object with a 360 video and turn it into a fully explorable 3D model—all directly in the Insta360 app. With high quality imaging and native 360 video recording, Insta360 cameras make 3D Gaussian Splatting scanning more accessible, so it's easier than ever to capture and preserve your memories in an immersive 3D format.

Insta360

66,498 Aufrufe • vor 25 Tagen

With Insta360's Spatial Capture officially released in the US today, we can finally announce our partnership with Insta360. Yes — Insta360’s Spatial Capture feature is powered by KIRI Engine. By combining 360° cameras with KIRI Engine’s 3D Gaussian Splatting technology, we’re making it dramatically easier to capture large real-world environments and turn them into immersive 3D spaces. One of the biggest breakthroughs here is the capture workflow. Compared with existing 360° Gaussian Splatting workflows, Spatial Capture is dramatically simpler We believe 360 cameras can become one of the most natural ways to capture the world in 3D — and this collaboration is a big step toward making that happen.

With Insta360's Spatial Capture officially released in the US today, we can finally announce our partnership with Insta360. Yes — Insta360’s Spatial Capture feature is powered by KIRI Engine. By combining 360° cameras with KIRI Engine’s 3D Gaussian Splatting technology, we’re making it dramatically easier to capture large real-world environments and turn them into immersive 3D spaces. One of the biggest breakthroughs here is the capture workflow. Compared with existing 360° Gaussian Splatting workflows, Spatial Capture is dramatically simpler We believe 360 cameras can become one of the most natural ways to capture the world in 3D — and this collaboration is a big step toward making that happen.

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17,857 Aufrufe • vor 16 Tagen

Google Maps helps you get from A to B. SuperSplat makes you feel like you're actually there. Built on 3D Gaussian splatting tech from PlayCanvas, photorealistic captures of real-world places stream to your browser in seconds. The best bit? It's all free and open source! [1/2]

Google Maps helps you get from A to B. SuperSplat makes you feel like you're actually there. Built on 3D Gaussian splatting tech from PlayCanvas, photorealistic captures of real-world places stream to your browser in seconds. The best bit? It's all free and open source! [1/2]

Will Eastcott

105,685 Aufrufe • vor 2 Monaten

SpecNeRF: Gaussian Directional Encoding for Specular Reflections paper page: Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance of glossy surfaces, especially under complex lighting of indoor environments. Unlike existing methods, which typically assume distant lighting like an environment map, we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly, our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result, it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields, which helps decompose appearance into more physically meaningful components.

SpecNeRF: Gaussian Directional Encoding for Specular Reflections paper page: Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance of glossy surfaces, especially under complex lighting of indoor environments. Unlike existing methods, which typically assume distant lighting like an environment map, we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly, our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result, it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields, which helps decompose appearance into more physically meaningful components.

AK

33,948 Aufrufe • vor 2 Jahren