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Found 7 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.
A Fusion of Variational Distribution Priors and Saliency Map Replay for Continual 3D Reconstruction
To this end, we propose a continual learning-based 3D reconstruction method where our goal is to design a model using Variational Priors that can still reconstruct the previously seen classes reasonably even after training on new classes.
Physically Plausible 3D Human-Scene Reconstruction from Monocular RGB Image using an Adversarial Learning Approach
This paper proposes using an implicit feature representation of the scene elements to distinguish a physically plausible alignment of humans and objects from an implausible one.
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 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.
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.
