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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 ⬇️

59,377 次观看 • 2 年前 •via X (Twitter)

10 条评论

Dreaming Tulpa 🥓👑 的头像
Dreaming Tulpa 🥓👑2 年前

Project Page: Code:

maddie🌙 的头像
maddie🌙2 年前

looks like pure shite, good work

Dennis 的头像
Dennis2 年前

sick

Kiri 的头像
Kiri2 年前

Wow!!!!

Charles Williamson 的头像
Charles Williamson2 年前

This looks interesting!

Tang the Wandering Hermit 的头像
Tang the Wandering Hermit2 年前

Man, roblox creator gonna be nuts lmao

🤐 的头像
🤐2 年前

惊艳!效果真好

Pseudonym 🦅 的头像
Pseudonym 🦅2 年前

Lego as a benchmark is quite clever. It’s easy to spot differences and design consistency. Lego builds either look like Lego or are clearly wrong. Pretty incredible a non specialized system can even get this close. Reminds me of the whole fine tuning versus prompting debate.

Ericreator 的头像
Ericreator2 年前

is the code live? i only saw a readme and lic

Dreaming Tulpa 🥓👑 的头像
Dreaming Tulpa 🥓👑2 年前

Nope. Not live.

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

141,058 次观看 • 2 年前