Leveraging Transformer-based Sequence Labeling for Robust Co-Speech Gesture Detection

Keskeiset käsitteet

A novel framework that reframes co-speech gesture detection as a multi-phase sequence labeling task, leveraging Transformer encoders and Conditional Random Fields to capture the inherent sequential and contextual nature of gestures.

Tiivistelmä

This study proposes a novel framework for co-speech gesture detection that conceptualizes the task as a multi-phase sequence labeling problem, in contrast to the prevalent binary classification approach. The key components of the framework are:

Representing time windows of skeletal movements as spatio-temporal graphs, which are then embedded using Spatio-Temporal Graph Convolutional Networks (ST-GCNs).

Applying Transformer encoders to learn contextualized embeddings of the movement sequences, capturing longer-term temporal dependencies.

Employing Conditional Random Fields (CRFs) for structured prediction, leveraging the sequential dependencies between the different gesture phases (preparation, stroke, retraction).

The authors evaluate their framework on a large dataset of co-speech gestures in naturalistic face-to-face dialogues. The results demonstrate that the proposed multi-phase sequence labeling approach significantly outperforms strong baseline models in detecting gesture strokes. Furthermore, the use of Transformer encoders to learn contextual embeddings from movement sequences substantially improves gesture unit detection.
These findings highlight the framework's capacity to capture the fine-grained dynamics of co-speech gesture phases, paving the way for more nuanced and accurate gesture detection and analysis.

Tilastot

The average duration of gesture strokes in the dataset is 0.58 seconds.
The dataset contains 6106 manually annotated gesture strokes.
The dataset is highly imbalanced, with 89.4% of the time windows labeled as Neutral (no gesture).

Lainaukset

"Gestures are inherent ingredients of face-to-face communication that serve many functions, such as illustrating objects or actions, emphasizing verbal expression, or indicating direction."
"Gestures are not abrupt events; instead, they unfold over time, often following a predictable pattern of phases."

Tärkeimmät oivallukset

Co-Speech Gesture Detection through Multi-Phase Sequence Labeling

by Esam... klo arxiv.org 04-24-2024

https://arxiv.org/pdf/2308.10680.pdf

Syvällisempiä Kysymyksiä

How can the proposed framework be extended to handle more complex and diverse co-speech gestures, beyond the referential gestures in the current dataset?

The proposed framework for co-speech gesture detection can be extended to handle more complex and diverse gestures by incorporating additional layers of complexity in the modeling process. One way to achieve this is by introducing a hierarchical approach to gesture detection. This hierarchical approach can involve multiple levels of abstraction, where lower levels capture basic gesture features, and higher levels combine these features to recognize more complex gestures. By incorporating hierarchical modeling, the framework can learn to detect gestures with varying levels of intricacy and nuance.
Furthermore, the framework can benefit from incorporating a wider range of gesture types and contexts in the training data. By diversifying the dataset to include a broader spectrum of gestures, such as emblematic, beat, or metaphoric gestures, the model can learn to generalize better to unseen gestures. Additionally, introducing adversarial training techniques can help the model become more robust to variations in gesture styles and expressions.
Moreover, leveraging transfer learning techniques can enhance the framework's ability to handle diverse gestures. Pre-training the model on a large dataset containing a wide variety of gestures and then fine-tuning it on the specific dataset of interest can help the model adapt to different gesture types effectively. This approach can enable the framework to learn common patterns across different types of gestures and improve its overall performance on diverse co-speech gestures.

How could the insights from this work on gesture detection be applied to other domains, such as sign language recognition or human-robot interaction, where understanding the temporal dynamics of body movements is crucial?

The insights gained from this work on gesture detection can be highly valuable for applications in sign language recognition and human-robot interaction, where understanding the temporal dynamics of body movements is essential.
In the context of sign language recognition, the framework's emphasis on sequential modeling and capturing the fine-grained dynamics of gestures can be directly applied. By adapting the framework to recognize sign language gestures, it can effectively analyze the sequential nature of sign language expressions, including the preparation, execution, and conclusion phases of each sign. This can lead to more accurate and context-aware sign language recognition systems that consider the temporal dynamics of gestures.
For human-robot interaction, the framework's ability to detect and analyze co-speech gestures can enhance the robot's understanding of human communication cues. By integrating the framework into a robot's perception system, the robot can interpret human gestures in real-time, enabling more natural and intuitive interactions. Understanding the temporal dynamics of body movements can help the robot respond appropriately to gestures, improving the overall communication and collaboration between humans and robots.
Additionally, the insights from this work can be leveraged in fields such as emotion recognition, behavior analysis, and virtual reality applications, where capturing and interpreting body movements play a crucial role in understanding human interactions and experiences.

What other modalities, such as audio or eye-gaze, could be integrated into the framework to further improve gesture detection performance?

Integrating additional modalities such as audio and eye-gaze into the framework can significantly enhance gesture detection performance by providing complementary information and context. Here are some ways these modalities can be integrated:

Audio Modality: By incorporating audio data into the framework, the model can leverage speech cues to enhance gesture detection. Audio features such as speech content, intonation, and prosody can provide valuable context for interpreting gestures. Techniques like audio-visual fusion can be employed to combine audio and visual information for more robust gesture recognition.

Eye-Gaze Modality: Eye-gaze information can offer insights into a person's focus of attention and intention, which can aid in understanding the context of gestures. Integrating eye-gaze tracking data with visual cues can help the model infer the relationship between gaze direction and specific gestures. This integration can improve the model's ability to interpret gestures accurately in different conversational contexts.

Multimodal Fusion: Combining information from multiple modalities, including visual, audio, and eye-gaze data, through multimodal fusion techniques like late fusion, early fusion, or attention mechanisms, can lead to a more comprehensive and robust gesture detection system. By fusing information from different modalities, the framework can capture a richer representation of human communication cues, enhancing the overall performance of gesture detection.