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📢GaussianGPT (ECCV'26) Code Release📢 What if 3D scenes worked like language? Generate full 3D Gaussian scenes - from scratch or from partial - token by token! 🔗 🌐 Great work by Nicolas von Lützow, Barbara Roessle, Katharina Schmid

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Matthias Niessner

50,770 subscribers

58,546 次观看 • 3 个月前 •via X (Twitter)

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12 条评论

科学怪人HC 的头像
科学怪人HC3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid 有点像3d打印高斯一样

Chukwudalu Dumebi-Kachikwu 的头像
Chukwudalu Dumebi-Kachikwu3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid This is amazing

whatsupfranks 的头像
whatsupfranks3 个月前

@RadianceFields @nicolasvluetzow @barbara_roessle @katha_schmid Are the weights released?

Kristian Calloway 的头像
Kristian Calloway2 个月前

Hello Matthias, I'd love to work with you on a piece but I can't send you a dm. Please send me one whenever you are free

Matthias Niessner 的头像
Matthias Niessner2 个月前

Just send me an email?

Sai Kiran Dev 的头像
Sai Kiran Dev3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid Cool

ζ Pedram ζ 的头像
ζ Pedram ζ3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid Nice

Nick Cassab 的头像
Nick Cassab3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid Very cool

AI Mastery Guide 的头像
AI Mastery Guide3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid Treating 3D scenes like a language and generating them token by token is such a clean way to think about the problem.

imaginary 的头像
imaginary3 个月前

designed a camera that could capture 3d scenes rather than 2d snapshots of them. all you have to do is measure every photon's angle of incidence and frequency that the camera captures. You forgo the lens, too, and with some algorithms, have the camera build the scene in one shot.

goodtothinkwith 的头像
goodtothinkwith3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid That is really cool

Geospatial FM 的头像
Geospatial FM3 个月前

@nicolasvluetzow @barbara_roessle @katha_schmid Mind blown and I don’t even know what this means

相关视频

📢GaussianGPT: autoregressive 3D Gaussian scene generation. We introduce a GPT-style model that directly generates 3D Gaussian scenes, token by token, in a series of small, discrete decision steps. Generation, completion, and large-scale outpainting in a single pipeline. Unlike diffusion-based approaches, GaussianGPT explicitly models the scene distribution at every step, allowing for quite flexible scene synthesis. 🌐 ▶️ Great work by Nicolas von Lützow, Barbara Roessle, Katharina Schmid
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📢GaussianGPT: autoregressive 3D Gaussian scene generation. We introduce a GPT-style model that directly generates 3D Gaussian scenes, token by token, in a series of small, discrete decision steps. Generation, completion, and large-scale outpainting in a single pipeline. Unlike diffusion-based approaches, GaussianGPT explicitly models the scene distribution at every step, allowing for quite flexible scene synthesis. 🌐 ▶️ Great work by Nicolas von Lützow, Barbara Roessle, Katharina Schmid

Matthias Niessner

152,802 次观看 • 7 个月前

📢 GenRecon Code Release 📢 Few images in → complete, high-fidelity 3D scene out! GenRecon builds a generative prior on full scenes, resulting in unprecedented 3D reconstruction quality. 🔗 🌐 📄
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📢 GenRecon Code Release 📢 Few images in → complete, high-fidelity 3D scene out! GenRecon builds a generative prior on full scenes, resulting in unprecedented 3D reconstruction quality. 🔗 🌐 📄

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40,570 次观看 • 4 个月前

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📢📢GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction📢📢 Reconstructing high-fidelity 3D scenes from sparse RGB input is hard. It needs a strong 3D prior! We reformulate multi-view scene reconstruction as conditional 3D generation over overlapping spatial chunks, lifting posed image features into a generative shape prior via 3D conditioning. As an example prior, we build on Trellis2, and train it such that its reconstruction is pixel aligned and matches from all views. GenRecon achieves unprecedented reconstruction quality from any sparse RGB input sequence, even from a phone capture. The reconstruction also includes PBR materials which facilitates relighting and virtual object insertion. Amazing work by Katharina Schmid, Nicolas von Lützow, Jozef, Angela Dai

Matthias Niessner

18,447 次观看 • 5 个月前

HoloDreamer can generate enclosed 3D scenes from text descriptions! It does so by first creating a high-quality equirectangular panorama and then rapidly reconstructing the 3D scene using 3D Gaussian Splatting. Links ⬇️
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HoloDreamer can generate enclosed 3D scenes from text descriptions! It does so by first creating a high-quality equirectangular panorama and then rapidly reconstructing the 3D scene using 3D Gaussian Splatting. Links ⬇️

Dreaming Tulpa 🥓👑

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📢 LinPrim: Linear Primitives for Differentiable Volumetric Rendering 📢 We use octahedra or tetrahedra as explicit as volumetric building blocks for gradient-based novel view synthesis - as an alternative to 3D Gaussians with discrete, bounded geometry. We show how it can be used to reconstruct photorealistic scenes, and introduce a corresponding differentiable CUDA rasterizer that enables real-time rendering. On real-world scenes, LinPrim achieves comparable image quality with fewer primitives, adding a practical polyhedral option to the 3D scene representation toolbox and expanding the known design space. 🌍 🎥 Amazing work by Nicolas von Lützow!

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11,425 次观看 • 1 年前

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📢WorldMesh is accepted to #ECCV2026, and we're releasing the code today! 🎉 Led by mschneider456: navigable, multi-room 3D scenes from a text prompt, with a mesh scaffold conditioning image diffusion for global consistency + photorealistic detail 👇

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