Julien Gaubil
@jgaubil.bsky.social
PhD student at École Polytechnique
Interested in Computer Vision, Geometry, and learning both at the same time
https://www.jgaubil.com/
Interested in Computer Vision, Geometry, and learning both at the same time
https://www.jgaubil.com/
DUSt3R et al. are impressive, but how do they actually work? We investigate this in our project 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘔𝘶𝘭𝘵𝘪-𝘝𝘪𝘦𝘸 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴!
We share findings on the iterative nature of reconstruction, the roles of cross and self-attention, and the emergence of correspondences across the network [1/8] ⬇️
We share findings on the iterative nature of reconstruction, the roles of cross and self-attention, and the emergence of correspondences across the network [1/8] ⬇️
𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗩𝗶𝗲𝘄 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀
Michal Stary, Julien Gaubil, Ayush Tewari, Vincent Sitzmann
arxiv.org/abs/2510.24907
Trending on www.scholar-inbox.com
Michal Stary, Julien Gaubil, Ayush Tewari, Vincent Sitzmann
arxiv.org/abs/2510.24907
Trending on www.scholar-inbox.com
November 4, 2025 at 7:40 PM
DUSt3R et al. are impressive, but how do they actually work? We investigate this in our project 𝘜𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨 𝘔𝘶𝘭𝘵𝘪-𝘝𝘪𝘦𝘸 𝘛𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘦𝘳𝘴!
We share findings on the iterative nature of reconstruction, the roles of cross and self-attention, and the emergence of correspondences across the network [1/8] ⬇️
We share findings on the iterative nature of reconstruction, the roles of cross and self-attention, and the emergence of correspondences across the network [1/8] ⬇️
Reposted by Julien Gaubil
𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗩𝗶𝗲𝘄 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀
Michal Stary, Julien Gaubil, Ayush Tewari, Vincent Sitzmann
arxiv.org/abs/2510.24907
Trending on www.scholar-inbox.com
Michal Stary, Julien Gaubil, Ayush Tewari, Vincent Sitzmann
arxiv.org/abs/2510.24907
Trending on www.scholar-inbox.com
October 31, 2025 at 7:00 AM
𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗠𝘂𝗹𝘁𝗶-𝗩𝗶𝗲𝘄 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀
Michal Stary, Julien Gaubil, Ayush Tewari, Vincent Sitzmann
arxiv.org/abs/2510.24907
Trending on www.scholar-inbox.com
Michal Stary, Julien Gaubil, Ayush Tewari, Vincent Sitzmann
arxiv.org/abs/2510.24907
Trending on www.scholar-inbox.com
Reposted by Julien Gaubil
Stary and Gaubil et al., "Understanding multi-view transformers"
We use Dust3r as a black box. This work looks under the hood at what is going on. The internal representations seem to "iteratively" refine towards the final answer. Quite similar to what goes on in point cloud net
We use Dust3r as a black box. This work looks under the hood at what is going on. The internal representations seem to "iteratively" refine towards the final answer. Quite similar to what goes on in point cloud net
October 30, 2025 at 9:00 PM
Stary and Gaubil et al., "Understanding multi-view transformers"
We use Dust3r as a black box. This work looks under the hood at what is going on. The internal representations seem to "iteratively" refine towards the final answer. Quite similar to what goes on in point cloud net
We use Dust3r as a black box. This work looks under the hood at what is going on. The internal representations seem to "iteratively" refine towards the final answer. Quite similar to what goes on in point cloud net
Reposted by Julien Gaubil
1/n🚀Gaussians > Differentiable function > Mesh?
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
September 8, 2025 at 11:35 AM
1/n🚀Gaussians > Differentiable function > Mesh?
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
Check out our new work: MILo: Mesh-In-the-Loop Gaussian Splatting!
🎉Accepted to SIGGRAPH Asia 2025 (TOG)
MILo is a novel differentiable framework that extracts meshes directly from Gaussian parameters during training.
🧵👇
Reposted by Julien Gaubil
🚨🚨 WiGRAPH CONFERENCE COFFEE @ SIGGRAPH '25 🚨🚨
Sign up now to be randomly matched with peers for a SIGGRAPH conference coffee!
Sign up now to be randomly matched with peers for a SIGGRAPH conference coffee!
🎉☕ Announcing WiGRAPH Conference Coffees: SIGGRAPH 2025 Edition! ☕🎉
Are you a researcher of an underrepresented gender registered for SIGGRAPH? Do you want an opportunity to network with your peers? Learn more and sign up here:
www.wigraph.org/events/2025-...
Are you a researcher of an underrepresented gender registered for SIGGRAPH? Do you want an opportunity to network with your peers? Learn more and sign up here:
www.wigraph.org/events/2025-...
WiGRAPH Conference Coffee: Sign-Up
WiGRAPH is organizing a SIGGRAPH 2025 conference coffee!
Are you a researcher of a gender that is underrepresented at SIGGRAPH? Would you like for an opportunity to network with peers? Sign up for th...
tinyurl.com
July 10, 2025 at 4:39 PM
🚨🚨 WiGRAPH CONFERENCE COFFEE @ SIGGRAPH '25 🚨🚨
Sign up now to be randomly matched with peers for a SIGGRAPH conference coffee!
Sign up now to be randomly matched with peers for a SIGGRAPH conference coffee!
Reposted by Julien Gaubil
💻We've released the code for our #CVPR2025 paper MAtCha!
🍵MAtCha reconstructs sharp, accurate and scalable meshes of both foreground AND background from just a few unposed images (eg 3 to 10 images)...
...While also working with dense-view datasets (hundreds of images)!
🍵MAtCha reconstructs sharp, accurate and scalable meshes of both foreground AND background from just a few unposed images (eg 3 to 10 images)...
...While also working with dense-view datasets (hundreds of images)!
April 3, 2025 at 10:33 AM
💻We've released the code for our #CVPR2025 paper MAtCha!
🍵MAtCha reconstructs sharp, accurate and scalable meshes of both foreground AND background from just a few unposed images (eg 3 to 10 images)...
...While also working with dense-view datasets (hundreds of images)!
🍵MAtCha reconstructs sharp, accurate and scalable meshes of both foreground AND background from just a few unposed images (eg 3 to 10 images)...
...While also working with dense-view datasets (hundreds of images)!
I think there is too much (good) content pouring in the Vision/3D communities daily to read thoroughly while preserving room for creativity. I believe what's important is to know 'what exists', without necessarily knowing all the details, to have an accurate picture of what remains to be done
Sometimes reading Hamming makes me sad, because I recognize myself in this quote.
February 25, 2025 at 5:31 PM
I think there is too much (good) content pouring in the Vision/3D communities daily to read thoroughly while preserving room for creativity. I believe what's important is to know 'what exists', without necessarily knowing all the details, to have an accurate picture of what remains to be done
Reposted by Julien Gaubil
Sometimes reading Hamming makes me sad, because I recognize myself in this quote.
February 25, 2025 at 9:17 AM
Sometimes reading Hamming makes me sad, because I recognize myself in this quote.