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Found 16 papers, 3 papers with code
Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation
Positional encodings are employed to capture the high frequency information of the encoded signals in implicit neural representation (INR).
Latent-Shift: Gradient of Entropy Helps Neural Codecs
End-to-end image/video codecs are getting competitive compared to traditional compression techniques that have been developed through decades of manual engineering efforts.
Entropy Coding Improvement for Low-complexity Compressive Auto-encoders
End-to-end image and video compression using auto-encoders (AE) offers new appealing perspectives in terms of rate-distortion gains and applications.
RQAT-INR: Improved Implicit Neural Image Compression
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 compression rates compared to the decades long traditional codecs such as AVC, HEVC or VVC.
Reducing The Mismatch Between Marginal and Learned Distributions in Neural Video Compression
In this paper, we propose to evaluate the amortization gap for three state-of-the-art ML video compression methods.
Reducing The Amortization Gap of Entropy Bottleneck In End-to-End Image Compression
The decoder is also learned as a deep trainable network, and the reconstructed image measures the distortion.
Video Coding Using Learned Latent GAN Compression
We leverage the generative capacity of GANs such as StyleGAN to represent and compress a video, including intra and inter compression.
Semantic Unfolding of StyleGAN Latent Space
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
Additionally, we demonstrate that the proposed encoder is especially well-suited for inversion and editing on videos.
FacialFilmroll: High-resolution multi-shot video editing
We present FacialFilmroll, a solution for spatially and temporally consistent editing of faces in one or multiple shots.
Learning Perceptual Compression of Facial Video
We leverage the generative capacity of GANs such as StyleGAN to represent and compress each video frame (intra compression), as well as the successive differences between frames (inter compression).
Semantic and Geometric Unfolding of StyleGAN Latent Space
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.
A Latent Transformer for Disentangled Face Editing in Images and Videos
Previous works that attempt to tackle this problem may suffer from the entanglement of facial attributes and the loss of the person's identity.
JUMPS: Joints Upsampling Method for Pose Sequences
Human Pose Estimation is a low-level task useful forsurveillance, human action recognition, and scene understandingat large.
Split and Match: Example-Based Adaptive Patch Sampling for Unsupervised Style Transfer
This paper presents a novel unsupervised method to transfer the style of an example image to a source image.
