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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

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53,841 просмотров • 4 дней назад •via X (Twitter)

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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
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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

152,004 просмотров • 3 месяцев назад

📢 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. 🔗 🌐 📄

Matthias Niessner

38,896 просмотров • 10 дней назад

📢📢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
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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

17,999 просмотров • 1 месяц назад

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 ⬇️

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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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📢 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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📢𝐋𝟑𝐃𝐆: 𝐋𝐚𝐭𝐞𝐧𝐭 𝟑𝐃 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧📢 #SIGGRAPHAsia We propose a generative diffusion model for 3D Gaussians. Key is a learnt latent space which substantially reduces the complexity of the diffusion process, thus facilitating room-scale scene generation! Great work by Barbara Roessle in with Norman Müller, Angela Dai, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder!
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📢𝐋𝟑𝐃𝐆: 𝐋𝐚𝐭𝐞𝐧𝐭 𝟑𝐃 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐃𝐢𝐟𝐟𝐮𝐬𝐢𝐨𝐧📢 #SIGGRAPHAsia We propose a generative diffusion model for 3D Gaussians. Key is a learnt latent space which substantially reduces the complexity of the diffusion process, thus facilitating room-scale scene generation! Great work by Barbara Roessle in with Norman Müller, Angela Dai, Lorenzo Porzi, Samuel Rota Bulò, Peter Kontschieder!

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Gemini 3.0 Pro is at great 3D design and front-end. It can generate complex 3D scenes and Three.js objects from text prompts. Here's a nice retro-future example from cris.
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Gemini 3.0 Pro is at great 3D design and front-end. It can generate complex 3D scenes and Three.js objects from text prompts. Here's a nice retro-future example from cris.

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90,189 просмотров • 6 месяцев назад

📢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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📢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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📢 FaceAnything (ECCV 2026) Code Release 📢 Turn any image sequence into high-fidelity 4D face reconstructions, without controlled capture rigs. Try it on Hugging Face & reconstruct your face in 4D! 🔗 🤗 🌐

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47,685 просмотров • 12 дней назад

PhysX-Anything: creates simulation-ready, articulated 3D assets from a single image; VLM-based model using a new 3D representation that reduces token count by 193x
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HUGE NEWS for 3D Gaussian Splatting! 📢 The PlayCanvas Engine adopts SOGS for 20x Compression of 3DGS. 🗜️ Load HUGE splat scenes even on mobile! 📱 Read More:

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