ScanTalk: 3D Talking Heads from Unregistered Scans
This addresses the need for more flexible and realistic 3D facial animations for applications like virtual reality or film, though it appears incremental as it builds on existing architectures to overcome a specific limitation.
The paper tackles the problem of generating 3D talking heads from speech, which is constrained by fixed topologies in existing methods, and presents ScanTalk, a framework that animates faces with arbitrary topologies using DiffusionNet, achieving realistic results comparable to state-of-the-art techniques.
Speech-driven 3D talking heads generation has emerged as a significant area of interest among researchers, presenting numerous challenges. Existing methods are constrained by animating faces with fixed topologies, wherein point-wise correspondence is established, and the number and order of points remains consistent across all identities the model can animate. In this work, we present \textbf{ScanTalk}, a novel framework capable of animating 3D faces in arbitrary topologies including scanned data. Our approach relies on the DiffusionNet architecture to overcome the fixed topology constraint, offering promising avenues for more flexible and realistic 3D animations. By leveraging the power of DiffusionNet, ScanTalk not only adapts to diverse facial structures but also maintains fidelity when dealing with scanned data, thereby enhancing the authenticity and versatility of generated 3D talking heads. Through comprehensive comparisons with state-of-the-art methods, we validate the efficacy of our approach, demonstrating its capacity to generate realistic talking heads comparable to existing techniques. While our primary objective is to develop a generic method free from topological constraints, all state-of-the-art methodologies are bound by such limitations. Code for reproducing our results, and the pre-trained model are available at https://github.com/miccunifi/ScanTalk .