3D Facial Animation Synthesis

3D facial animation synthesis aims to generate realistic and expressive three-dimensional facial movements from various input modalities, such as speech, text, or emotion labels. Current research heavily emphasizes probabilistic models, including diffusion models and variational autoencoders (VAEs), to capture the inherent variability and non-deterministic nature of human facial expressions, often incorporating hierarchical representations to better model different facial regions and activity levels. This field is significant for its applications in virtual reality, animation, and human-computer interaction, driving advancements in both data generation (large-scale datasets with diverse emotional expressions) and model architectures for improved realism and controllability.