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Found 6 papers, 0 papers with code
Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis
NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin.
Microdosing: Knowledge Distillation for GAN based Compression
Recently, significant progress has been made in learned image and video compression.
Blind Image Restoration with Flow Based Priors
In contrast to this, we propose using normalizing flows to model the distribution of the target content and to use this as a prior in a maximum a posteriori (MAP) formulation.
Lossy Image Compression with Normalizing Flows
However, state-of-the-art solutions for deep image compression typically employ autoencoders which map the input to a lower dimensional latent space and thus irreversibly discard information already before quantization.
Disentangled Dynamic Representations from Unordered Data
We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input.
Unsupervised Deep Representations for Learning Audience Facial Behaviors
In this paper, we present an unsupervised learning approach for analyzing facial behavior based on a deep generative model combined with a convolutional neural network (CNN).
