70 citations · 87 across the 14 of their papers we have counts for
17 papers
Spying Across Chiplets: Side-Channel Attacks in 2.5/3D Integrated Systems
Giorgio Di Natale, Christelle Rabache, Pierre-Louis Hellier +4
Advanced packaging and chiplet-based integration are increasingly adopted to build complex heterogeneous systems beyond the limits of monolithic scaling. While these architectures…
Latent-Shift: Gradient of Entropy Helps Neural Codecs
Muhammet Balcilar, Bharath Bhushan Damodaran, Karam Naser +2
End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts. These t…
HuMoT: Human Motion Representation using Topology-Agnostic Transformers for Character Animation Retargeting
Lucas Mourot, Ludovic Hoyet, François Le Clerc +1
Motion retargeting is the long-standing problem in character animation that consists in transferring and adapting the motion of a source character to another target character. A ty…
RQAT-INR: Improved Implicit Neural Image Compression
Bharath Bhushan Damodaran, Muhammet Balcilar, Franck Galpin +1
Deep variational autoencoders for image and video compression have gained significant attraction in the recent years, due to their potential to offer competitive or better compress…
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders
Franck Galpin, Muhammet Balcilar, Frédéric Lefebvre +2
End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications. While most complex models are…
Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression
Muhammet Balcilar, Bharath Bhushan Damodaran, Pierre Hellier
During the last four years, we have witnessed the success of end-to-end trainable models for image compression. Compared to decades of incremental work, these machine learning (ML)…
Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression
Muhammet Balcilar, Bharath Damodaran, Pierre Hellier
End-to-end deep trainable models are about to exceed the performance of the traditional handcrafted compression techniques on videos and images. The core idea is to learn a non-lin…
UnderPressure: Deep Learning for Foot Contact Detection, Ground Reaction Force Estimation and Footskate Cleanup
Lucas Mourot, Ludovic Hoyet, François Le Clerc +1
Human motion synthesis and editing are essential to many applications like film post-production. However, they often introduce artefacts in motions, which can be detrimental to the…
Video Coding Using Learned Latent GAN Compression
Mustafa Shukor, Bharath Bhushan Damodaran, Xu Yao +1
We propose in this paper a new paradigm for facial video compression. We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intr…
Semantic Unfolding of StyleGAN Latent Space
Mustafa Shukor, Xu Yao, Bharath Bushan Damodaran +1
Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to an input real image…
Feature-Style Encoder for Style-Based GAN Inversion
Xu Yao, Alasdair Newson, Yann Gousseau +1
We propose a novel architecture for GAN inversion, which we call Feature-Style encoder. The style encoder is key for the manipulation of the obtained latent codes, while the featur…
A Survey on Deep Learning for Skeleton-Based Human Animation
L. Mourot, L. Hoyet, F. Le Clerc +2
Human character animation is often critical in entertainment content production, including video games, virtual reality or fiction films. To this end, deep neural networks drive mo…
FacialFilmroll: High-resolution multi-shot video editing
Bharath Bhushan Damodaran, Emmanuel Jolly, Gilles Puy +6
We present FacialFilmroll, a solution for spatially and temporally consistent editing of faces in one or multiple shots. We build upon unwrap mosaic [Rav-Acha et al. 2008] by speci…
Semantic and Geometric Unfolding of StyleGAN Latent Space
Mustafa Shukor, Xu Yao, Bharath Bhushan Damodaran +1
Generative adversarial networks (GANs) have proven to be surprisingly efficient for image editing by inverting and manipulating the latent code corresponding to a natural image. Th…
A Latent Transformer for Disentangled Face Editing in Images and Videos
Xu Yao, Alasdair Newson, Yann Gousseau +1
High quality facial image editing is a challenging problem in the movie post-production industry, requiring a high degree of control and identity preservation. Previous works that…
JUMPS: Joints Upsampling Method for Pose Sequences
Lucas Mourot, François Le Clerc, Cédric Thébault +1
Human Pose Estimation is a low-level task useful forsurveillance, human action recognition, and scene understandingat large. It also offers promising perspectives for the animation…
High Resolution Face Age Editing
Xu Yao, Gilles Puy, Alasdair Newson +2
Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some o…