正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

PixWorld Unifying 3D Scene Generation and Reconstruction in Pixel Space

AK

526,875 subscribers

16,750 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

5 条评论

AK 的头像

AK3 个月前

paper:

Isabelle E-Z 的头像

Isabelle E-Z3 个月前

The pixel space approach is elegant. Unifying 3D generation and reconstruction will have real implications for robotics and automation.

Jonathan Korstad 的头像

Jonathan Korstad3 个月前

👀

Ferbin 的头像

Ferbin3 个月前

The big unlock is editing a room like a photo. Change the couch, keep the shadows, don't rebuild the whole world.

AI Mastery Guide 的头像

AI Mastery Guide3 个月前

1042x faster and better quality is a pretty rare combo to see in the same result.

相关视频

Pixel-perfect 3D generation is here. 🧵 Tencent’s new Free Local Pixal3D project every pixel directly into 3D space. No more "AI hallucinations"—just sub-pixel faithful reconstruction. ✅ Open Weights ✅ Local (24GB VRAM) ✅ Matches silhouettes perfectly Is this the new benchmark for open-source 3D? Full workflow and test: 👇 #3DAI #Pixal3D #GameDev #Tencen #OpenSource #3DModeling

Pixel-perfect 3D generation is here. 🧵 Tencent’s new Free Local Pixal3D project every pixel directly into 3D space. No more "AI hallucinations"—just sub-pixel faithful reconstruction. ✅ Open Weights ✅ Local (24GB VRAM) ✅ Matches silhouettes perfectly Is this the new benchmark for open-source 3D? Full workflow and test: 👇 #3DAI #Pixal3D #GameDev #Tencen #OpenSource #3DModeling

Stefan 3D AI

23,210 次观看 • 5 个月前

Nvidia presents LLaMA-Mesh Unifying 3D Mesh Generation with Language Models

Nvidia presents LLaMA-Mesh Unifying 3D Mesh Generation with Language Models

AK

113,609 次观看 • 1 年前

👾 UniForge3D: Joint 3D Reconstruction and Generation in a Shared Anchor-Guided Space ❤ Thanks to Wenjue Chen, Wenyue Chen, Heliang Zheng, Rongfei Jia, Yuan Liu, Meng Li 🌐 🧬 📄 📦

👾 UniForge3D: Joint 3D Reconstruction and Generation in a Shared Anchor-Guided Space ❤ Thanks to Wenjue Chen, Wenyue Chen, Heliang Zheng, Rongfei Jia, Yuan Liu, Meng Li 🌐 🧬 📄 📦

camenduru

15,728 次观看 • 10 天前

📢📢GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction📢📢 Reconstructing high-fidelity 3D scenes from sparse RGB input is hard. It needs a strong 3D prior! We reformulate multi-view scene reconstruction as conditional 3D generation over overlapping spatial chunks, lifting posed image features into a generative shape prior via 3D conditioning. As an example prior, we build on Trellis2, and train it such that its reconstruction is pixel aligned and matches from all views. GenRecon achieves unprecedented reconstruction quality from any sparse RGB input sequence, even from a phone capture. The reconstruction also includes PBR materials which facilitates relighting and virtual object insertion. Amazing work by Katharina Schmid, Nicolas von Lützow, Jozef, Angela Dai

📢📢GenRecon: Bridging Generative Priors for Multi-View 3D Scene Reconstruction📢📢 Reconstructing high-fidelity 3D scenes from sparse RGB input is hard. It needs a strong 3D prior! We reformulate multi-view scene reconstruction as conditional 3D generation over overlapping spatial chunks, lifting posed image features into a generative shape prior via 3D conditioning. As an example prior, we build on Trellis2, and train it such that its reconstruction is pixel aligned and matches from all views. GenRecon achieves unprecedented reconstruction quality from any sparse RGB input sequence, even from a phone capture. The reconstruction also includes PBR materials which facilitates relighting and virtual object insertion. Amazing work by Katharina Schmid, Nicolas von Lützow, Jozef, Angela Dai

Matthias Niessner

18,447 次观看 • 5 个月前

Pixal3D Pixel-Aligned 3D Generation from Images

Pixal3D Pixel-Aligned 3D Generation from Images

AK

31,427 次观看 • 5 个月前

SAM 3D enables accurate 3D reconstruction from a single image, supporting real-world applications in editing, robotics, and interactive scene generation. Matt, a SAM 3D researcher, explains how the two-model design makes this possible for both people and complex environments. 🔗 Read the SAM 3D Objects research paper: 🔗 Read the SAM 3D Body research paper:

SAM 3D enables accurate 3D reconstruction from a single image, supporting real-world applications in editing, robotics, and interactive scene generation. Matt, a SAM 3D researcher, explains how the two-model design makes this possible for both people and complex environments. 🔗 Read the SAM 3D Objects research paper: 🔗 Read the SAM 3D Body research paper:

AI at Meta

18,031 次观看 • 11 个月前

LEGO-Anything Coding Agents for 3D Scene Reconstruction paper:

LEGO-Anything Coding Agents for 3D Scene Reconstruction paper:

AK

100,023 次观看 • 24 天前

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

66,918 次观看 • 1 年前

3D scene reconstructions by NVIDIA. ArtiFixer - repairs artifacts and extends sparse views via Wan 2.1. - high-fidelity inpainting in occluded regions - gens hundreds of consistent frames in a single pass - 3D Gaussian Splatting for navigable scene reconstruction Makes the 3D environment look photorealistic and fully navigable for VR/AR. It basically turns a broken 3D model into a polished, professional scene.

3D scene reconstructions by NVIDIA. ArtiFixer - repairs artifacts and extends sparse views via Wan 2.1. - high-fidelity inpainting in occluded regions - gens hundreds of consistent frames in a single pass - 3D Gaussian Splatting for navigable scene reconstruction Makes the 3D environment look photorealistic and fully navigable for VR/AR. It basically turns a broken 3D model into a polished, professional scene.

Wildminder

16,086 次观看 • 4 个月前

Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.

Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.

ViggleAI

1,712,714 次观看 • 5 天前

Live monocular 3D reconstruction from camera feed, with multi-frame stitching and relative positioning (frame buffer of 4)... running on M3 mac cpu at 24fps... visualised using Rerun next up... realtime semantic navigation in 3d space...

Live monocular 3D reconstruction from camera feed, with multi-frame stitching and relative positioning (frame buffer of 4)... running on M3 mac cpu at 24fps... visualised using Rerun next up... realtime semantic navigation in 3d space...

ud

22,080 次观看 • 9 个月前

just a pixel in space

just a pixel in space

pale kirill

99,739 次观看 • 7 个月前

WaterSplatting: Fast Underwater 3D Scene Reconstruction Using Gaussian Splatting Project: Method ⬇️ 1 I 2

WaterSplatting: Fast Underwater 3D Scene Reconstruction Using Gaussian Splatting Project: Method ⬇️ 1 I 2

MrNeRF

12,190 次观看 • 2 年前

📢Face Anything: 4D Face Reconstruction from Any Image Sequence Transformer model for 4D face reconstruction and dense tracking: - predict canonical facial coordinates per pixel - tracking as reconstruction in canonical space - geometry + correspondences in one forward pass Key idea: a shared canonical space across frames - correspondences as nearest neighbors - no motion or deformation estimation Stable geometry and tracking, even under large expressions and viewpoint changes - check out our results! 🌐 ▶️ Great work by Umut Kocasarı, Simon Giebenhain, Richard Shaw

📢Face Anything: 4D Face Reconstruction from Any Image Sequence Transformer model for 4D face reconstruction and dense tracking: - predict canonical facial coordinates per pixel - tracking as reconstruction in canonical space - geometry + correspondences in one forward pass Key idea: a shared canonical space across frames - correspondences as nearest neighbors - no motion or deformation estimation Stable geometry and tracking, even under large expressions and viewpoint changes - check out our results! 🌐 ▶️ Great work by Umut Kocasarı, Simon Giebenhain, Richard Shaw

Matthias Niessner

61,927 次观看 • 6 个月前

📢 GenRecon Code Release 📢 Few images in → complete, high-fidelity 3D scene out! GenRecon builds a generative prior on full scenes, resulting in unprecedented 3D reconstruction quality. 🔗 🌐 📄

📢 GenRecon Code Release 📢 Few images in → complete, high-fidelity 3D scene out! GenRecon builds a generative prior on full scenes, resulting in unprecedented 3D reconstruction quality. 🔗 🌐 📄

Matthias Niessner

40,515 次观看 • 3 个月前

Spatial 3D AI is live on 🥽✨ You can now bring 3D AI Agents and 3D Assets straight into your real-world physical space in augmented reality. Powered by NVIDIA AI for lightning-fast 3D generation. Watch it in action below 👇

Spatial 3D AI is live on 🥽✨ You can now bring 3D AI Agents and 3D Assets straight into your real-world physical space in augmented reality. Powered by NVIDIA AI for lightning-fast 3D generation. Watch it in action below 👇

three.ws

12,106 次观看 • 2 个月前

Let's remove VAEs and ViTs from video models! 🚀 Introducing 𝗣𝗶𝘅𝗲𝗹𝗨𝗠𝗠: an encoder-free unified multimodal model for image and video understanding and generation, directly in pixel space. Code and model available today! 🌐 📄

Let's remove VAEs and ViTs from video models! 🚀 Introducing 𝗣𝗶𝘅𝗲𝗹𝗨𝗠𝗠: an encoder-free unified multimodal model for image and video understanding and generation, directly in pixel space. Code and model available today! 🌐 📄

Cong Wei

87,736 次观看 • 23 天前

🚀 Our NeurIPS '24 work, Large Spatial Model (LSM), is here! LSM performs semantic 3D reconstruction in just 0.1s, processing unposed data via feed-forward 3D reconstruction. 👉It leverages large-scale 3D datasets with minimal annotations, defining a 3D latent space. We are continuously exploring how this explicit 3D representation can further enhance reasoning and robotic learning. 🔗 Try our online Gradio demo with your own data at #NeurIPS2024 #3DReconstruction

🚀 Our NeurIPS '24 work, Large Spatial Model (LSM), is here! LSM performs semantic 3D reconstruction in just 0.1s, processing unposed data via feed-forward 3D reconstruction. 👉It leverages large-scale 3D datasets with minimal annotations, defining a 3D latent space. We are continuously exploring how this explicit 3D representation can further enhance reasoning and robotic learning. 🔗 Try our online Gradio demo with your own data at #NeurIPS2024 #3DReconstruction

Zhiwen(Aaron) Fan

44,051 次观看 • 2 年前