ScanTalk: 3D Talking Heads from Unregistered Scans

Kernekoncepter

ScanTalk introduces a novel framework for animating 3D faces in arbitrary topologies, overcoming limitations of fixed topologies and enhancing realism.

Resumé

ScanTalk presents a groundbreaking approach to speech-driven 3D facial animation. The framework allows for the animation of any 3D face, even raw scans, without being constrained by specific topologies. By leveraging DiffusionNet architecture, ScanTalk offers flexibility and realism in generating 3D talking heads. The model is trained on multiple datasets with varying topologies, demonstrating its ability to generalize across different mesh structures. Through quantitative evaluations and user studies, ScanTalk showcases promising results in lip-syncing fidelity and naturalness. The framework addresses challenges in topology robustness and holds potential for diverse applications in virtual reality and video game graphics.

Statistik

VOCAset: 320 training samples, 80 validation samples, 80 test samples
BIWI6: 400 training samples, 80 validation samples, 80 test samples
Multiface: 410 training samples, 100 validation samples, 100 test samples
DiffusionNet Encoder: Hidden size of 32
Bi-LSTM: 3 layers with hidden size of 32
DiffusionNet Decoder: Composed of four blocks

Citater

"ScanTalk extends the applicability of deep speech-driven facial animations by addressing the challenges of topology robustness."
"Our model demonstrates comparable quantitative and qualitative results with other state-of-the-art methods across three distinct datasets."
"ScanTalk showcases promising results in lip-syncing fidelity and naturalness."

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ScanTalk

by Federico Noc... kl. arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10942.pdf

Dybere Forespørgsler

How can ScanTalk's capability to animate unregistered meshes be further expanded to include expressions?

To expand ScanTalk's capability to include expressions in animating unregistered meshes, a few strategies can be considered. One approach could involve incorporating additional training data that includes varied facial expressions along with speech data. This would allow the model to learn the correlation between different expressions and speech patterns, enabling it to generate more expressive animations.
Furthermore, introducing a mechanism for capturing emotional cues from speech audio could enhance the model's ability to animate facial expressions accurately. By analyzing intonations, pitch variations, and other vocal features related to emotions, ScanTalk could dynamically adjust facial movements based on the emotional content of the spoken words.
Additionally, integrating techniques like emotion detection algorithms or sentiment analysis into the training process could provide valuable insights into how different emotions manifest in facial expressions. By leveraging these tools during training, ScanTalk could learn to generate realistic and contextually appropriate facial animations corresponding to various emotional states expressed in speech.

How can unsupervised training strategies be implemented to enhance ScanTalk's performance?

Unsupervised training strategies offer potential avenues for enhancing ScanTalk's performance by reducing reliance on labeled data and allowing for more flexible learning processes. One way is through self-supervised learning approaches where the model learns from unlabeled data by predicting certain properties of the input without explicit supervision. For example, ScanTalk could employ pretext tasks such as predicting temporal coherence or geometric transformations within sequences of 3D face meshes.
Another method is through adversarial training where auxiliary networks are introduced to provide feedback signals that guide the main network towards generating more realistic outputs. By incorporating adversarial components into its architecture, ScanTalk can improve its ability to produce natural-looking facial animations without explicitly annotated ground truth labels.
Moreover, semi-supervised learning techniques can also be beneficial for enhancing performance by leveraging both labeled and unlabeled data during training. By effectively utilizing available information across different datasets with varying levels of supervision, ScanTalk can adapt better to diverse scenarios and generalize well beyond its initial training conditions.

What ethical considerations should be taken into account when developing technologies like ScanTalk?

When developing technologies like ScanTalk for 3D facial animation driven by speech audio, several ethical considerations must be addressed:

Privacy Concerns: Ensure that user consent is obtained before using their voice or image data for animation purposes.

Bias Mitigation: Take measures to prevent biases in dataset collection and algorithmic decision-making processes that may perpetuate stereotypes or discrimination.

Transparency: Provide clear explanations of how user data is collected, stored, and used within the technology.

Security Measures: Implement robust security protocols to safeguard sensitive user information from unauthorized access or misuse.

Accountability: Establish mechanisms for accountability in case of unintended consequences arising from technology usage.

6 .Accessibility: Ensure that technologies like Scan Talk are inclusive and accessible for users with diverse needs including those with disabilities.
By addressing these ethical considerations proactively throughout development stages,
Scan Talk developers can promote responsible use of technology while fostering trust
and transparency among users stakeholders alike..