MambaTalk: State Space Models for Gesture Synthesis

Główne pojęcia

State space models enhance gesture synthesis quality and diversity.

Streszczenie

Gesture synthesis is crucial in human-computer interaction, requiring synchronization between speech and gestures. Recent advancements leverage state space models to improve long sequences of gestures with low latency. MambaTalk introduces a two-stage modeling strategy with discrete motion priors to refine generated gestures. Multiple Mamba blocks enhance latent space representations for diverse and rhythmic gestures. The method matches or exceeds state-of-the-art models' performance through extensive experiments.

Statystyki

Recent works in co-speech gesture generation have shown great progress [9, 38, 58, 57, 2, 52].
RNN-based models often struggle with the long-term forgetting issue [44].
Transformer-based models depend heavily on subtle positional encoding to capture the order of input elements [36, 41].
Extensive experiments demonstrate the effectiveness of the proposed method.
Our method outperforms its counterparts in terms of MSE and LVD.

Cytaty

"Gesture synthesis is a critical area of research in human-computer interaction (HCI), which has very broad application prospects."
"We are the first to explore the potential of the selective scan mechanism for gesture synthesis."
"Our method ensures the creation of expressive gestures that are not only natural-looking but also in sync with the rhythm of speech."

Kluczowe wnioski z

MambaTalk

by Zunnan Xu,Yu... o arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09471.pdf

Głębsze pytania

How can state space models be further optimized for real-time gesture synthesis applications

State space models can be further optimized for real-time gesture synthesis applications by focusing on reducing computational complexity and latency. One approach could involve refining the selective scan mechanism to enhance its efficiency in capturing temporal dependencies across multiple time steps. Additionally, exploring techniques to streamline the training process and improve model convergence rates would be beneficial. Implementing hardware acceleration strategies or parallel processing methods could also help reduce inference times, making real-time gesture synthesis more feasible.

What are potential drawbacks or limitations when using multiple modules for animating different body parts

Using multiple modules for animating different body parts in gesture synthesis can introduce potential drawbacks such as increased complexity and latency. Coordinating the movements of various body parts generated by separate modules may lead to inconsistencies or unnatural transitions between gestures. Managing synchronization issues between these modules could pose a challenge, impacting the overall coherence and realism of the synthesized gestures. Furthermore, maintaining consistency in style and quality across different body segments might require additional effort in training and fine-tuning each module.

How can advancements in gesture synthesis impact other fields beyond human-computer interaction

Advancements in gesture synthesis have the potential to impact various fields beyond human-computer interaction. In film production, realistic co-speech gestures can enhance character animations and storytelling, creating more engaging visual narratives. In robotics, incorporating expressive gestures can improve human-robot interactions by enabling robots to convey emotions effectively. Virtual reality applications could benefit from lifelike gestural interfaces that enhance user immersion and interaction experiences. Moreover, advancements in gesture synthesis technology may find applications in healthcare for rehabilitation exercises or therapy sessions where non-verbal communication plays a crucial role in patient engagement and feedback.