70 citations · 87 across the 14 of their papers we have counts for
6 papers · 1 filter
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…