#cvpr2023

Anya Rossi

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✨Introducing Neuralangelo✨ A new #AI model to turn 2D video from any device -- cell phone to drone capture -- into 3D structures with intricate details. New from #NVIDIAResearch #CVPR2023 Read more:

✨Introducing Neuralangelo✨ A new #AI model to turn 2D video from any device -- cell phone to drone capture -- into 3D structures with intricate details. New from #NVIDIAResearch #CVPR2023 Read more:

NVIDIA AI Developer

497,873 просмотров • 3 лет назад

We relived Michelangelo in neural network style!! Super excited to finally share Neuralangelo 🛠 -- 3D surface reconstruction from RGB videos with extremely high fidelity! 🤩 To appear at #CVPR2023 project page: paper: 🧵⬇️ (1/4)

We relived Michelangelo in neural network style!! Super excited to finally share Neuralangelo 🛠 -- 3D surface reconstruction from RGB videos with extremely high fidelity! 🤩 To appear at #CVPR2023 project page: paper: 🧵⬇️ (1/4)

253,341 просмотров • 3 лет назад

Excited to share Norman Müller's DiffRF: Rendering-guided 3D Radiance Field Diffusion #CVPR2023 highlight! 2D diffusion is great, but what about 3D? We show radiance field diffusion with rendering guidance for consistent and editable 3D synthesis. Vid:

Excited to share Norman Müller's DiffRF: Rendering-guided 3D Radiance Field Diffusion #CVPR2023 highlight! 2D diffusion is great, but what about 3D? We show radiance field diffusion with rendering guidance for consistent and editable 3D synthesis. Vid:

Matthias Niessner

117,759 просмотров • 3 лет назад

The robot climbs stairs🏯, steps over stones 🪨, and runs in the wild🏞️, all in one policy, without any remote control! Our #CVPR2023 Highlight paper achieves this by using RL + a 3D Neural Volumetric Memory (NVM) trained with view synthesis!

The robot climbs stairs🏯, steps over stones 🪨, and runs in the wild🏞️, all in one policy, without any remote control! Our #CVPR2023 Highlight paper achieves this by using RL + a 3D Neural Volumetric Memory (NVM) trained with view synthesis!

113,006 просмотров • 3 лет назад

Super excited to share that my first project as a PhD student was accepted at CVPR 2023! #CVPR2023 😄 We show how to recover accurate camera poses from as few as 3 images! More details to come! w/ Jason Y. Zhang Andrea Tagliasacchi 🇨🇦 Igor Gilitschenski David Lindell

Super excited to share that my first project as a PhD student was accepted at CVPR 2023! #CVPR2023 😄 We show how to recover accurate camera poses from as few as 3 images! More details to come! w/ Jason Y. Zhang Andrea Tagliasacchi 🇨🇦 Igor Gilitschenski David Lindell

100,077 просмотров • 3 лет назад

🚨Crowded scenes are notoriously difficult for #ComputerVision models. In our new work lead by Zhou Mu & Lucas Stoffl we developed a novel approach called BUCTD that is state-of-the-art on crowded 🐒🐠&🕺human pose estimation benchmarks!🔥 #CVPR2023 workshop

🚨Crowded scenes are notoriously difficult for #ComputerVision models. In our new work lead by Zhou Mu & Lucas Stoffl we developed a novel approach called BUCTD that is state-of-the-art on crowded 🐒🐠&🕺human pose estimation benchmarks!🔥 #CVPR2023 workshop

Mackenzie Weygandt Mathis, PhD

67,207 просмотров • 3 лет назад

(1/2) Excited to share "Learning Neural Parametric Head Models" #CVPR2023! We capture over 5200 high-quality 3D human head scans from which we build a neural parametric head model that disentangles & expressions and deformations.

(1/2) Excited to share "Learning Neural Parametric Head Models" #CVPR2023! We capture over 5200 high-quality 3D human head scans from which we build a neural parametric head model that disentangles & expressions and deformations.

Matthias Niessner

53,279 просмотров • 3 лет назад

Monocular depth estimation is “impossible” because one image can’t measure depth geometrically. Our iDisc #CVPR2023 can group pixels w/o supervision and learn depth inductive bias on groups. We get LiDAR-like (but denser) depth from single images! More:

Monocular depth estimation is “impossible” because one image can’t measure depth geometrically. Our iDisc #CVPR2023 can group pixels w/o supervision and learn depth inductive bias on groups. We get LiDAR-like (but denser) depth from single images! More:

52,234 просмотров • 3 лет назад

Accepted by #CVPR2023! X-Decoder is the FIRST generalist decoder that supports all segmentation tasks (ins/sem/pano/ref) in OPEN VOCABULARY, both inter- AND intra-image VL tasks, and even helps instruct image inpainting/editing! New demo below and more at

Accepted by #CVPR2023! X-Decoder is the FIRST generalist decoder that supports all segmentation tasks (ins/sem/pano/ref) in OPEN VOCABULARY, both inter- AND intra-image VL tasks, and even helps instruct image inpainting/editing! New demo below and more at

51,930 просмотров • 3 лет назад

Capturing out-of-focus photos is sooo frustrating! 😩 Check out our work on defocus control with dual-camera 📸 #CVPR2023 Our method can refocus and adjust the depth of field AFTER you capture the photograph! 🤩 Paper: Project:

Capturing out-of-focus photos is sooo frustrating! 😩 Check out our work on defocus control with dual-camera 📸 #CVPR2023 Our method can refocus and adjust the depth of field AFTER you capture the photograph! 🤩 Paper: Project:

41,330 просмотров • 3 лет назад

Segment Anything is fun and powerful but still requires full mask annotation for supervised training. Our new work MaskFreeVIS appearing at #CVPR2023 shows we can get accurate video instance segmentation WITHOUT any video or image mask annotations. More at

Segment Anything is fun and powerful but still requires full mask annotation for supervised training. Our new work MaskFreeVIS appearing at #CVPR2023 shows we can get accurate video instance segmentation WITHOUT any video or image mask annotations. More at

41,103 просмотров • 3 лет назад

We are excited to present "Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM" at #CVPR2023 next week. Great work by Wang Hengyi and Jingwen Wang. Check out our code and evaluation benchmark! UCL Computer Science UKRI CDT in Foundational AI

We are excited to present "Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM" at #CVPR2023 next week. Great work by Wang Hengyi and Jingwen Wang. Check out our code and evaluation benchmark! UCL Computer Science UKRI CDT in Foundational AI

Lourdes Agapito

36,863 просмотров • 3 лет назад