3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in face-generation
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Use these libraries to find face-generation models and implementations
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The results show that FExGAN-Meta robustly generates and classifies the images of Meta-Humans for the simple as well as the complex facial expressions.
Experiments show that this generative framework for generating 3D facial expression sequences that can be conditioned on different inputs to animate an arbitrary 3D face mesh has learned to generate realistic, quality expressions solely from the dataset of relatively small size, improving over the state-of-the-art methods.
A generative model architecture is proposed which robustly generates a set of facial expressions for multiple character identities and explores the possibilities of generating complex expressions by combining the simple ones.
Adding a benchmark result helps the community track progress.