Postdoctoral researcher in computer vision at Ecole polytechnique.
I'm interested in 3D Reconstruction, Radiance Fields, Gaussian splatting, 3D Scene Rendering, 3D Scene Understanding, etc.
Webpage: https://anttwo.github.io/
Antoine Guédon
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Surflo: Consistent 3D Surface Flow Model with Global State
@antoine-guedon.bsky.social, Shu Nakamura, @nicolasdufour.bsky.social, Jiahui Lei, Ko Nishino, @akanazawa.bsky.social
arxiv.org/abs/2606.13644
CVPR@Paris 2026 🇫🇷 — June 1st, co-organised by ELLIS Unit Paris. A one-day local event ahead of CVPR, open to all. Oral & poster sessions for CVPR 2026, CVPR workshops & ICLR 2026 papers.
🔗 https://cvprinparis.github.io/CVPR2026InParis/
Thrilled to share that MIRO is accepted to ICML 2026 @icmlconf.bsky.social ! 🎉
By training on the reward scores, we can simply condition the model on high rewards at inference time to guarantee top-tier, aligned outputs.
We’ve updated our paper with some additional results!
🔴FROM BLOBS TO SPOKES🚲
We released paper and code for GaussianWrapping, our latest work on RGB-to-mesh!
We introduce explicit geometric field formulas for Gaussians (occupancy&normals), allowing for fast and sharp surface reco (see bicycle spokes).
So happy about this work!🤩
I’ll be at #SIGGRAPHAsia2025 next week presenting our paper MILo! Join the Neural Fields and Surface Reconstruction session on Tuesday, December 16.
If you’ll be in Hong Kong and would like to discuss research, or grab a coffee ☕️ feel free to reach out.
We introduce MIRO: a new paradigm for T2I model alignment integrating reward conditioning into pretraining, eliminating the need for separate fine-tuning/RL stages. This single-stage approach offers unprecedented efficiency and control.
- 19x faster convergence ⚡
- 370x less FLOPS than FLUX-dev 📉
Familiar names among #ICCV2025 Outstanding Reviewers from our team 😇
Antoine Guédon @antoine-guedon.bsky.social
Sinisa Stekovic
Renaud Marlet
👏
@iccv.bsky.social
iccv.thecvf.com/Conferences/...
🚨 arxiv.org/abs/2604.06129
PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
This paper is the result of doing a lab-wide hackathon on an idea I've had for some time. Probably the paper with the highest number of authors I've ever done.
It's a CVPR Findings 26.
Thread 🧵👇
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates input tokens into a comp...
The default paradigm of post-training text-to-image generators includes post-hoc selection of generated images, and subsequent training with one reward model to align the generator to the reward, typi...
1/n 🧵 Introducing Gaussian Wrapping — a principled framework for extracting high-quality meshes from 3DGS! 🚲
We recover thin structures, like bicycle spokes, where all prior methods fail.
Follow the thread for a brief overview and links!
We introduce MIRO: a new paradigm for T2I model alignment integrating reward conditioning into pretraining, eliminating the need for separate fine-tuning/RL stages. This single-stage approach offers unprecedented efficiency and control.
- 19x faster convergence ⚡
- 370x less FLOPS than FLUX-dev 📉
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.
🧵👇
Diego Gomez
Nicolas Dufour
Video
Video
Antoine Guédon
CVPR@Paris 2026 🇫🇷 — June 1st, co-organised by ELLIS Unit Paris. A one-day local event ahead of CVPR, open to all. Oral & poster sessions for CVPR 2026, CVPR workshops & ICLR 2026 papers.
🔗 https://cvprinparis.github.io/CVPR2026InParis/
