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Found 5 papers, 0 papers with code
Speech-driven Facial Animation using Cascaded GANs for Learning of Motion and Texture
Current state-of-the-art methods fail to generate realistic animation from any speech on unknown faces due to their poor gen-eralization over different facial characteristics, languages, and accents.
Emotion-Controllable Generalized Talking Face Generation
We propose a graph convolutional neural network that uses speech content feature, along with an independent emotion input to generate emotion and speech-induced motion on facial geometry-aware landmark representation.
Identity-Preserving Realistic Talking Face Generation
The necessary attributes of having a realistic face animation are 1) audio-visual synchronization (2) identity preservation of the target individual (3) plausible mouth movements (4) presence of natural eye blinks.
Lifting 2d Human Pose to 3d : A Weakly Supervised Approach
Few approaches have utilized training images from both 3d and 2d pose datasets in a weakly-supervised manner for learning 3d poses in unconstrained settings.
Lifting 2d Human Pose to 3d : A WeaklySupervised Approach
Our method uses re-projection error minimizationas a constraint to predict the 3d locations of body joints, andthis is crucial for training on data where the 3d ground-truth isnot present.
