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Found 10 papers, 5 papers with code
INR-V: A Continuous Representation Space for Video-based Generative Tasks
In this work, we evaluate the space learned by INR-V on diverse generative tasks such as video interpolation, novel video generation, video inversion, and video inpainting against the existing baselines.
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Video Inpainting
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Extreme-scale Talking-Face Video Upsampling with Audio-Visual Priors
We show that when we process this $8\times8$ video with the right set of audio and image priors, we can obtain a full-length, $256\times256$ video.
Domain Impression: A Source Data Free Domain Adaptation Method
This practical scenario creates a bottleneck in the domain adaptation problem.
Self Supervision for Attention Networks
In recent years, the attention mechanism has become a fairly popular concept and has proven to be successful in many machine learning applications.
Stochastic Talking Face Generation Using Latent Distribution Matching
Indeed, just having the ability to generate a single talking face would make a system almost robotic in nature.
BaSIL: Learning Incrementally using a Bayesian Memory-Based Streaming Approach
However, these methods mainly focus on incremental batch learning.
Speech Prediction in Silent Videos using Variational Autoencoders
Understanding the relationship between the auditory and visual signals is crucial for many different applications ranging from computer-generated imagery (CGI) and video editing automation to assisting people with hearing or visual impairments.
Collaborative Learning to Generate Audio-Video Jointly
Towards this, we propose a method that demonstrates that we are able to generate naturalistic samples of video and audio data by the joint correlated generation of audio and video modalities.
Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation
Understanding unsupervised domain adaptation has been an important task that has been well explored.
Lip-to-Speech Synthesis for Arbitrary Speakers in the Wild
With the help of multiple powerful discriminators that guide the training process, our generator learns to synthesize speech sequences in any voice for the lip movements of any person.
