17 citations · 30 across the 22 of their papers we have counts for
23 papers
Balancing Frequencies and Pixels in Flow Matching
Lucas Degeorge, Paul Couairon, Arijit Ghosh +3
Natural images follow a spectral distribution: most signal energy lies in the low spatial frequencies, while the perceptually important structures such as textures and edge…
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…
DiO: Distilling Masked Diffusion Models into One-step Generator
Yuanzhi Zhu, Xi Wang, Stéphane Lathuilière +1
Masked Diffusion Models (MDMs) have emerged as a powerful generative modeling technique. Despite their remarkable results, they typically suffer from slow inference with several st…
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…
AKiRa: Augmentation Kit on Rays for optical video generation
Xi Wang, Robin Courant, Marc Christie +1
Recent advances in text-conditioned video diffusion have greatly improved video quality. However, these methods offer limited or sometimes no control to users on camera aspects, in…
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…
LEAD: Latent Realignment for Human Motion Diffusion
Nefeli Andreou, Xi Wang, Victoria Fernández Abrevaya +3
Our goal is to generate realistic human motion from natural language. Modern methods often face a trade-off between model expressiveness and text-to-motion alignment. Some align te…
Bridging Text and Image for Artist Style Transfer via Contrastive Learning
Zhi-Song Liu, Li-Wen Wang, Jun Xiao +1
Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making i…
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…
Long Story Short: Story-level Video Understanding from 20K Short Films
Ridouane Ghermi, Xi Wang, Vicky Kalogeiton +1
Recent developments in vision-language models have significantly advanced video understanding. Existing datasets and tasks, however, have notable limitations. Most datasets are con…
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.…
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…
FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild
Zhi-Song Liu, Robin Courant, Vicky Kalogeiton
Automatically understanding funny moments (i.e., the moments that make people laugh) when watching comedy is challenging, as they relate to various features, such as body language,…
Reward Function Design for Crowd Simulation via Reinforcement Learning
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré +1
Crowd simulation is important for video-games design, since it enables to populate virtual worlds with autonomous avatars that navigate in a human-like manner. Reinforcement learni…
BluNF: Blueprint Neural Field
Robin Courant, Xi Wang, Marc Christie +1
Neural Radiance Fields (NeRFs) have revolutionized scene novel view synthesis, offering visually realistic, precise, and robust implicit reconstructions. While recent approaches en…
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…
UGAE: A Novel Approach to Non-exponential Discounting
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré +1
The discounting mechanism in Reinforcement Learning determines the relative importance of future and present rewards. While exponential discounting is widely used in practice, non-…
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…
Understanding reinforcement learned crowds
Ariel Kwiatkowski, Vicky Kalogeiton, Julien Pettré +1
Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual a…
A Survey on Reinforcement Learning Methods in Character Animation
Ariel Kwiatkowski, Eduardo Alvarado, Vicky Kalogeiton +4
Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environme…
Name Your Style: An Arbitrary Artist-aware Image Style Transfer
Zhi-Song Liu, Li-Wen Wang, Wan-Chi Siu +1
Image style transfer has attracted widespread attention in the past few years. Despite its remarkable results, it requires additional style images available as references, making i…
Multiple Style Transfer via Variational AutoEncoder
Zhi-Song Liu, Vicky Kalogeiton, Marie-Paule Cani
Modern works on style transfer focus on transferring style from a single image. Recently, some approaches study multiple style transfer; these, however, are either too slow or fail…