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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 просмотров • 1 год назад •via X (Twitter)

Комментарии: 9

Фото профиля Andu 🇺🇦🇪🇺
Andu 🇺🇦🇪🇺1 год назад

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

Фото профиля Dreaming Tulpa 🥓👑
Dreaming Tulpa 🥓👑1 год назад

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

Фото профиля TomLikesRobots🤖
TomLikesRobots🤖1 год назад

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

Фото профиля Joshua Johnson
Joshua Johnson1 год назад

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

Фото профиля Supreme
Supreme1 год назад

yoooooooooo

Фото профиля lady mantis 💚⚔️💫
lady mantis 💚⚔️💫1 год назад

@BLUECOW009 Even mantises?

Фото профиля Dreaming Tulpa 🥓👑
Dreaming Tulpa 🥓👑1 год назад

@BLUECOW009 Maybe

Фото профиля saltxdiamond
saltxdiamond1 год назад

Jensen and team going crazy🔥🔥

Фото профиля Dreaming Tulpa 🥓👑
Dreaming Tulpa 🥓👑1 год назад

💯

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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.

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140,992 просмотров • 2 лет назад