1 citations · 4 across the 16 of their papers we have counts for
6 papers · 1 filter
Diffusable Latents from Structure-Agnostic Distillation
Adrien Ramanana Rahary, Nicolas Dufour, Patrick Pérez +1
Distilling pretrained foundation models into an autoencoder bottleneck improves latent diffusability, enabling diffusion models to converge faster and reach higher sample quality.…
The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation
Nicolas Dufour, Alexei A. Efros, Patrick Pérez
The Frechet Inception Distance (FID) is the de facto arbiter of image generation, yet most papers report just a single number from a single trained model using a single sampling se…
Surflo: Consistent 3D Surface Flow Model with Global State
Antoine Guédon, Shu Nakamura, Nicolas Dufour +3
Geometry is invariant to viewpoint, which makes any collection of images a redundant encoding of a single 3D state. Existing feed-forward reconstruction models fail to exploit this…
One View Is Enough: In-the-Wild Monocular Pretraining for Novel View Generation
Adrien Ramanana Rahary, Nicolas Dufour, Patrick Perez +1
Monocular novel-view synthesis has long required multi-view image pairs for supervision, limiting training to a narrow set of purpose-built datasets. We propose in-the-wild monocul…
PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
David Picard, Nicolas Dufour, Lucas Degeorge +14
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 inpu…
Generative Drifting is Secretly Score Matching: a Spectral and Variational Perspective
Erkan Turan, Nicolas Dufour, Maks Ovsjanikov
Generative Modeling via Drifting~\citep{deng2026drifting} has recently achieved state-of-the-art one-step image generation through a kernel-based drift operator, yet its success is…