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(1/3) 📢📢𝐆𝐆𝐇𝐞𝐚𝐝 𝐅𝐚𝐬𝐭 𝐚𝐧𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐥𝐢𝐳𝐚𝐛𝐥𝐞 𝟑𝐃 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐇𝐞𝐚𝐝𝐬📢📢 #SiggraphAsia'24 We generate photo-realistic 3D heads and render them with Gaussian Splatting at 1k resolution in real-time.

Matthias Niessner

48,009 subscribers

36,618 views • 1 year ago •via X (Twitter)

Education Arts Science & Technology #SiggraphAsia

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(1/2) 📢𝐍𝐏𝐆𝐀: 𝐍𝐞𝐮𝐫𝐚𝐥 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐫𝐢𝐜 𝐆𝐚𝐮𝐬𝐬𝐢𝐚𝐧 𝐀𝐯𝐚𝐭𝐚𝐫𝐬 📢 #SIGGRAPHAsia We leverage a neural parametric representation to facilitate precise control over 3D Gaussians to obtain high-fidelity avatars.

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30,741 views • 1 year ago

📢📢📢 Today I'll be giving a talk at the #CVPR2024 workshop on "Learning 3D with Multi-View Supervision" See you at 16:20 in Summit 331 Get a preview of the upcoming "3D Gaussian Splatting as Markov Chain Monte Carlo"

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Andrea Tagliasacchi

47,170 views • 2 years ago

📢📢📢introducing 𝐏𝐨𝐰𝐞𝐫 𝐅𝐨𝐚𝐦 A 3D representation that can be ray traced or rasterized in real time, with NO COMPROMISE in quality. - Project: - arXiv: Rasterized at 3DGS-class FPS Ray traced at Radiant Foam speeds

📢📢📢introducing 𝐏𝐨𝐰𝐞𝐫 𝐅𝐨𝐚𝐦 A 3D representation that can be ray traced or rasterized in real time, with NO COMPROMISE in quality. - Project: - arXiv: Rasterized at 3DGS-class FPS Ray traced at Radiant Foam speeds

Andrea Tagliasacchi

95,395 views • 3 months ago

📢We introduce “RefFusion”, a novel inpainting method for scenes reconstructed using 3D Gaussian Splatting. 🔗 TLDR: we personalize an image diffusion model to a given reference image and distill its knowledge to 3D through score distillation sampling.

📢We introduce “RefFusion”, a novel inpainting method for scenes reconstructed using 3D Gaussian Splatting. 🔗 TLDR: we personalize an image diffusion model to a given reference image and distill its knowledge to 3D through score distillation sampling.

Ashkan Mirzaei

34,700 views • 2 years ago

📢 We introduce PPISP, a physically plausible camera module for Gaussian Splatting that reduces floaters under varying appearance and predicts camera corrections for novel views such that they closely match what a real camera would output. #CV #3DGS

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Zan Gojcic

26,233 views • 6 months ago

Apple just trained a 3D Gaussian head reconstruction model on 10,000+ subjects. Feed-forward. No test-time optimization. New identity in, reconstructed Gaussian head out. The UV-parameterized Gaussian representation decouples the number of Gaussians from the number and resolution of input images, making it practical to train with many high resolution views. And the heads are not just static either: text-conditioned identity generation, plus blendshape-driven latent animation across identities. We've been building in the 3D Gaussian Splatting space for a while. The gap between "research demo" and "works on real people at scale" is closing fast.

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KIRI Engine - 3D Scanner App

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90,108 views • 2 years ago

We will be in ACM SIGGRAPH 2023 with "3D Gaussian Splatting for Real-Time Radiance Field Rendering", have you ever seen radiance fields with 100+ FPS and MipNeRF360 quality? Check out our website here:

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71,846 views • 1 year ago

3D Gaussian Splatting for Real-Time Radiance Field Rendering paper page: Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methods inevitably trade off speed for quality. For unbounded and complete scenes (rather than isolated objects) and 1080p resolution rendering, no current method can achieve real-time display rates. We introduce three key elements that allow us to achieve state-of-the-art visual quality while maintaining competitive training times and importantly allow high-quality real-time (>= 30 fps) novel-view synthesis at 1080p resolution. First, starting from sparse points produced during camera calibration, we represent the scene with 3D Gaussians that preserve desirable properties of continuous volumetric radiance fields for scene optimization while avoiding unnecessary computation in empty space; Second, we perform interleaved optimization/density control of the 3D Gaussians, notably optimizing anisotropic covariance to achieve an accurate representation of the scene; Third, we develop a fast visibility-aware rendering algorithm that supports anisotropic splatting and both accelerates training and allows realtime rendering. We demonstrate state-of-the-art visual quality and real-time rendering on several established datasets.

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633,674 views • 3 years ago

Fighters, new features have been added in Patch 1.10! 📢 Feature update - Photo Mode 📷 Snap the perfect shot with these new features: – Wetness/Grime adjustment – Expression and Gaze Direction adjustement – Gestures picker – Frame-by-frame adjustment (60 frames - 1 second)

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TEKKEN

73,147 views • 1 year ago

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90,948 views • 2 years ago

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

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25,279 views • 1 year ago

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