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Nvidia presents EdgeRunner! The method can generate high-quality 3D meshes with up to 4,000 faces at a spatial resolution of 512 from images and point-clouds.

173,103 次观看 • 1 年前 •via X (Twitter)

9 条评论

Andu 🇺🇦🇪🇺 的头像
Andu 🇺🇦🇪🇺1 年前

Do they ever open source this stuff? We have mesh anything that seems similar

Dreaming Tulpa 🥓👑 的头像
Dreaming Tulpa 🥓👑1 年前

Sometimes they do. But not often. Yeah, there is also a Mesh Anything V2.

TomLikesRobots🤖 的头像
TomLikesRobots🤖1 年前

Cool. That topology is cleaner than I thought it would be.

Joshua Johnson 的头像
Joshua Johnson1 年前

Interesting approach. Compressing 3D meshes down t a 1D sequence 👀.

Supreme 的头像
Supreme1 年前

yoooooooooo

lady mantis 💚⚔️💫 的头像
lady mantis 💚⚔️💫1 年前

@BLUECOW009 Even mantises?

Dreaming Tulpa 🥓👑 的头像
Dreaming Tulpa 🥓👑1 年前

@BLUECOW009 Maybe

saltxdiamond 的头像
saltxdiamond1 年前

Jensen and team going crazy🔥🔥

Dreaming Tulpa 🥓👑 的头像
Dreaming Tulpa 🥓👑1 年前

💯

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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

140,992 次观看 • 2 年前