most citedFace Aging via Diffusion-based Editing

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV20233 cited

Face Aging via Diffusion-based Editing

Xiangyi Chen, Stéphane Lathuilière

In this paper, we address the problem of face aging: generating past or future facial images by incorporating age-related changes to the given face. Previous aging methods rely sol…

cs.CV20232 cited

The Unreasonable Effectiveness of Large Language-Vision Models for Source-free Video Domain Adaptation

Giacomo Zara, Alessandro Conti, Subhankar Roy +3

Source-Free Video Unsupervised Domain Adaptation (SFVUDA) task consists in adapting an action recognition model, trained on a labelled source dataset, to an unlabelled target datas…

cs.CV2023

Test your samples jointly: Pseudo-reference for image quality evaluation

Marcelin Tworski, Stéphane Lathuilière

In this paper, we address the well-known image quality assessment problem but in contrast from existing approaches that predict image quality independently for every images, we pro…

cs.CV2023

Few-shot Semantic Image Synthesis with Class Affinity Transfer

Marlène Careil, Jakob Verbeek, Stéphane Lathuilière

Semantic image synthesis aims to generate photo realistic images given a semantic segmentation map. Despite much recent progress, training them still requires large datasets of ima…

cs.MM2022

A Hybrid Deep Animation Codec for Low-bitrate Video Conferencing

Goluck Konuko, Stéphane Lathuilière, Giuseppe Valenzise

Deep generative models, and particularly facial animation schemes, can be used in video conferencing applications to efficiently compress a video through a sparse set of keypoints,…

cs.CV20221 cited

Custom Structure Preservation in Face Aging

Guillermo Gomez-Trenado, Stéphane Lathuilière, Pablo Mesejo +1

In this work, we propose a novel architecture for face age editing that can produce structural modifications while maintaining relevant details present in the original image. We di…