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3D was either pretty, or fast. Now it’s BOTH! Meet Interactive Scenes built with Gaussian Splatting: 🔥Browser & Phone-Friendly: Hyperefficient and fast rendering everywhere 👌Embed Anywhere: 8-20MB streaming files (even smaller soon!) ✨Ultra High Quality offline NeRF renders & mesh exports 🍕Creating is as easy as capturing a video...

528,667 次观看 • 2 年前 •via X (Twitter)

10 条评论

Luma AI 的头像
Luma AI2 年前

⬆️Upgrade your best Luma captures to Interactive Scenes right in the app or on the web ✨And now, export UE and Splat files as well

Luma AI 的头像
Luma AI2 年前

Congratulations to @Snosixtytwo and @GKopanas, and INRIA for their incredible research on realtime 3D rendering!

Luma AI 的头像
Luma AI2 年前

Meet the spectacular new Metal 3D renderer! It's fast, beautiful, and so much fun to play with!

Luma AI 的头像
Luma AI2 年前

Meet the hyperefficient new WebGL renderer. It just _flies_ on phones old and new, and screams on laptops! Feat an intricate but kind humanoid by @ryanmhickman

Luma AI 的头像
Luma AI2 年前

Let’s bring Interactive Scenes everywhere!

Alex Carlier 的头像
Alex Carlier2 年前

Amazing work! Will try some captures asap 🔥🙌

polycam 的头像
polycam2 年前

Welcome to the club!

Martin Nebelong 的头像
Martin Nebelong2 年前

Incredible, can't wait to experiment with this 🔥❤️

bloomy 🌸 的头像
bloomy 🌸2 年前

i never thought the future would come this fast fr

Jon Booth 🧙‍♂️ 的头像
Jon Booth 🧙‍♂️2 年前

So you’re telling me soon we can build 3D sets and literally be digital directors and create anything… or is this the Now? Holy

相关视频

Nvidia announces GAvatar: Animatable 3D Gaussian Avatars with Implicit Mesh Learning paper page: Gaussian splatting has emerged as a powerful 3D representation that harnesses the advantages of both explicit (mesh) and implicit (NeRF) 3D representations. In this paper, we seek to leverage Gaussian splatting to generate realistic animatable avatars from textual descriptions, addressing the limitations (e.g., flexibility and efficiency) imposed by mesh or NeRF-based representations. However, a naive application of Gaussian splatting cannot generate high-quality animatable avatars and suffers from learning instability; it also cannot capture fine avatar geometries and often leads to degenerate body parts. To tackle these problems, we first propose a primitive-based 3D Gaussian representation where Gaussians are defined inside pose-driven primitives to facilitate animation. Second, to stabilize and amortize the learning of millions of Gaussians, we propose to use neural implicit fields to predict the Gaussian attributes (e.g., colors). Finally, to capture fine avatar geometries and extract detailed meshes, we propose a novel SDF-based implicit mesh learning approach for 3D Gaussians that regularizes the underlying geometries and extracts highly detailed textured meshes. Our proposed method, GAvatar, enables the large-scale generation of diverse animatable avatars using only text prompts. GAvatar significantly surpasses existing methods in terms of both appearance and geometry quality, and achieves extremely fast rendering (100 fps) at 1K resolution.

AK

140,960 次观看 • 2 年前