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