Загрузка видео...

Не удалось загрузить видео

На главную

Direct3D‑S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention

dreamingtulpa's profile picture

Dreaming Tulpa 🥓👑

56,462 subscribers

27,889 просмотров • 1 год назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Wow with new #iPhone15 Pro we’ll be able to record 3d spatial videos! #AppleEvent
0:10

Wow with new #iPhone15 Pro we’ll be able to record 3d spatial videos! #AppleEvent

iJustine

226,330 просмотров • 3 лет назад

🔥Spatial intelligence needs fast, *interactive* 3D world generation 🎮 — introducing WonderWorld: generating 3D scenes interactively following your movement and content requests, and see them in <10 seconds! 🧵1/6 Web: arXiv:
0:10

🔥Spatial intelligence needs fast, *interactive* 3D world generation 🎮 — introducing WonderWorld: generating 3D scenes interactively following your movement and content requests, and see them in <10 seconds! 🧵1/6 Web: arXiv:

Hong-Xing (Koven) Yu

169,188 просмотров • 2 лет назад

🔥Spatial intelligence requires world generation, and now we have the first comprehensive evaluation benchmark📏 for it! Introducing WorldScore: Unifying evaluation for 3D, 4D, and video models on world generation! 🧵1/7 Web: arxiv:
0:14

🔥Spatial intelligence requires world generation, and now we have the first comprehensive evaluation benchmark📏 for it! Introducing WorldScore: Unifying evaluation for 3D, 4D, and video models on world generation! 🧵1/7 Web: arxiv:

Hong-Xing (Koven) Yu

66,876 просмотров • 1 год назад

"Spann3R: 3D Reconstruction with Spatial Memory" In a nutshell: DUSt3R strikes again! Paper: Project: Method ⬇️
0:14

"Spann3R: 3D Reconstruction with Spatial Memory" In a nutshell: DUSt3R strikes again! Paper: Project: Method ⬇️

MrNeRF

52,560 просмотров • 2 лет назад

NVIDIA released Sol-Attn A training-free sparse attention method accelerating video generation inference by up to 2.1× while preserving visual quality, unifying dynamic routing and approximate correction in a single online-softmax pass.
0:10

NVIDIA released Sol-Attn A training-free sparse attention method accelerating video generation inference by up to 2.1× while preserving visual quality, unifying dynamic routing and approximate correction in a single online-softmax pass.

DailyPapers

23,144 просмотров • 1 месяц назад

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.
0:18

DimensionX: Create Any 3D and 4D Scenes from a Single Image with Controllable Video Diffusion TL;DR: Create 3/4DGS from Video Diffusion Note: Some first inference code released (not all yet). Contributions (cited): • We present DimensionX, a novel framework for generating photorealistic 3D and 4D scenes from only a single image using controllable video diffusion. • We propose ST-Director, which decouples the spatial and temporal priors in video diffusion models by learning (spatial and temporal) dimension-aware modules with our curated datasets. We further enhance the hybriddimension control with a training-free composition approach according to the essence of video diffusion denoising process. • To bridge the gap between video diffusion and real-world scenes, we design a trajectory-aware mechanism for 3D generation and an identity-preserving denoising approach for 4D generation, enabling more realistic and controllable scene synthesis. • Extensive experiments manifest that our DimensionX delivers superior performance in video, 3D, and 4D generation compared with baseline methods.

MrNeRF

17,062 просмотров • 1 год назад

MLLMs are great at understanding videos, but struggle with spatial reasoning—like estimating distances or tracking objects across time. the bottleneck? getting precise 3D spatial annotations on real videos is expensive and error-prone. introducing SIMS-V 🤖 [1/n]
0:14

MLLMs are great at understanding videos, but struggle with spatial reasoning—like estimating distances or tracking objects across time. the bottleneck? getting precise 3D spatial annotations on real videos is expensive and error-prone. introducing SIMS-V 🤖 [1/n]

Ellis Brown

16,480 просмотров • 10 месяцев назад

Cleaning Made Easy With This Genius Tool
0:18

Cleaning Made Easy With This Genius Tool

Agronomme Jean Baptiste🇬🇦 🌽🥦

11,739 просмотров • 3 месяцев назад

This handy print makes magnet installation much easier! Made with: ELEGOO #CentauriCarbon 3D printer 3D model from: HX8 3D Design
0:16

This handy print makes magnet installation much easier! Made with: ELEGOO #CentauriCarbon 3D printer 3D model from: HX8 3D Design

ELEGOO

25,397 просмотров • 9 месяцев назад

Metal Repair Made Easy with This Magic Sealant
0:12

Metal Repair Made Easy with This Magic Sealant

Chill_Broken

1,488,674 просмотров • 10 месяцев назад

Long drives made easy with thot b!txh
0:10

Long drives made easy with thot b!txh

Midnighthugo

11,471 просмотров • 1 год назад

Exploring !Boring stickers! in 3D, made with Omma AI.
0:13

Exploring !Boring stickers! in 3D, made with Omma AI.

Gábor Pribék

277,752 просмотров • 3 месяцев назад

hunting ghosts not made in 3D, but with AI
0:10

hunting ghosts not made in 3D, but with AI

INK

49,934 просмотров • 7 месяцев назад

Foldable donut made with Easy vertex anim plugin #UE5
0:19

Foldable donut made with Easy vertex anim plugin #UE5

Inu Games

61,916 просмотров • 10 месяцев назад

crazy.. AI made 3D Pixar animation with just one prompt
0:16

crazy.. AI made 3D Pixar animation with just one prompt

el.cine

3,749,587 просмотров • 4 месяцев назад

Beautiful WebGPU render made with ThreeJS using a 3D attractor formula
0:15

Beautiful WebGPU render made with ThreeJS using a 3D attractor formula

Tommyedz ΑΩ

33,720 просмотров • 1 год назад

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.
0:12

Wonderland: Navigating 3D Scenes from a Single Image Contributions: • First, we introduce a representation for controllable 3D generation by leveraging the generative priors from camera-guided video diffusion models. Unlike image models, video diffusion models are trained on extensive video datasets. This enables them to capture comprehensive spatial relationships within scenes across multiple views and embed a form of "3D awareness" in their latent space, which allows us to maintain 3D consistency in novel view synthesis. • Second, to achieve controllable novel view generation, we empower video models with precise control over specified camera motions. We introduce a novel dual-branch conditioning mechanism that effectively incorporates desired diverse camera trajectories into the video diffusion model. This enables expansion of a single image into a multi-view consistent capture of a 3D scene with precise pose control. • Third, to achieve efficient 3D reconstruction, we directly transform video latents into 3DGS. We propose a novel latent-based large reconstruction model (LaLRM) that lifts video latents to 3D in a feed-forward manner. With this design, during inference, our model directly predicts 3DGS from a single input image, effectively aligning the generation and reconstruction tasks—and bridging image space and 3D space—through the video latent space. Compared with reconstructing scenes from images, the video latent space offers a 256× spatial-temporal reduction while retaining essential and consistent 3D structural details. Such a high degree of compression is crucial, as it allows the LaLRM to handle a wider range of 3D scenes within the reconstruction framework, with the same memory constraints.

MrNeRF

52,849 просмотров • 1 год назад