1 citations · 4 across the 16 of their papers we have counts for
17 papers
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…
MIRO: MultI-Reward cOnditioned pretraining improves T2I quality and efficiency
Nicolas Dufour, Lucas Degeorge, Arijit Ghosh +2
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…
Training-Free Synthetic Data Generation with Dual IP-Adapter Guidance
Luc Boudier, Loris Manganelli, Eleftherios Tsonis +2
Few-shot image classification remains challenging due to the limited availability of labeled examples. Recent approaches have explored generating synthetic training data using text…
How far can we go with ImageNet for Text-to-Image generation?
L. Degeorge, A. Ghosh, N. Dufour +2
Recent text-to-image (T2I) generation models have achieved remarkable sucess by training on billion-scale datasets, following a `bigger is better' paradigm that prioritizes data qu…
Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation
Nicolas Dufour, David Picard, Vicky Kalogeiton +1
Global visual geolocation predicts where an image was captured on Earth. Since images vary in how precisely they can be localized, this task inherently involves a significant degre…
PoM: Efficient Image and Video Generation with the Polynomial Mixer
David Picard, Nicolas Dufour
Diffusion models based on Multi-Head Attention (MHA) have become ubiquitous to generate high quality images and videos. However, encoding an image or a video as a sequence of patch…
E.T. the Exceptional Trajectories: Text-to-camera-trajectory generation with character awareness
Robin Courant, Nicolas Dufour, Xi Wang +2
Stories and emotions in movies emerge through the effect of well-thought-out directing decisions, in particular camera placement and movement over time. Crafting compelling camera…
Don't drop your samples! Coherence-aware training benefits Conditional diffusion
Nicolas Dufour, Victor Besnier, Vicky Kalogeiton +1
Conditional diffusion models are powerful generative models that can leverage various types of conditional information, such as class labels, segmentation masks, or text captions.…
OpenStreetView-5M: The Many Roads to Global Visual Geolocation
Guillaume Astruc, Nicolas Dufour, Ioannis Siglidis +10
Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms. Yet, the absence of…
Analysis of Classifier-Free Guidance Weight Schedulers
Xi Wang, Nicolas Dufour, Nefeli Andreou +4
Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional prediction…
Machine Learning for Brain Disorders: Transformers and Visual Transformers
Robin Courant, Maika Edberg, Nicolas Dufour +1
Transformers were initially introduced for natural language processing (NLP) tasks, but fast they were adopted by most deep learning fields, including computer vision. They measure…
SCAM! Transferring humans between images with Semantic Cross Attention Modulation
Nicolas Dufour, David Picard, Vicky Kalogeiton
A large body of recent work targets semantically conditioned image generation. Most such methods focus on the narrower task of pose transfer and ignore the more challenging task of…