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Meta AI strikes again, with Relightable Gaussian Codec Avatars This is an update to the Meta Codec Avatars 2.0, building on 3D Gaussian Splatting. As a result, we get fully relightable real-time avatars, accurate at the hair strand level 🤯 More details below ⬇️⬇️

445,646 次观看 • 2 年前 •via X (Twitter)

8 条评论

Alex Carlier 的头像
Alex Carlier2 年前

Another video showing Relightable Gaussian Codec Avatars in more details

Alex Carlier 的头像
Alex Carlier2 年前

Follow @alexcarliera for more content about AI & AR! Project page:

Samuel Ekpe 的头像
Samuel Ekpe2 年前

Imagine this integrated into Zoom. No need for video calls again!

Alex Carlier 的头像
Alex Carlier2 年前

Haha everyone’s dream here it seems 😅

Ghost Pepper 的头像
Ghost Pepper2 年前

This is becoming a reality

Alex Carlier 的头像
Alex Carlier2 年前

Exactly!

Jerem Dev 的头像
Jerem Dev2 年前

I'm fascinated by the advancements in 3D Gaussian Splatting. Mind-blowing!

Alex Carlier 的头像
Alex Carlier2 年前

Yes it’s crazy how fast 3DGS have been adopted and improved 🤯

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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

140,960 次观看 • 2 年前