Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos (2204.01696v1)

Abstract: We propose to forecast future hand-object interactions given an egocentric video. Instead of predicting action labels or pixels, we directly predict the hand motion trajectory and the future contact points on the next active object (i.e., interaction hotspots). This relatively low-dimensional representation provides a concrete description of future interactions. To tackle this task, we first provide an automatic way to collect trajectory and hotspots labels on large-scale data. We then use this data to train an Object-Centric Transformer (OCT) model for prediction. Our model performs hand and object interaction reasoning via the self-attention mechanism in Transformers. OCT also provides a probabilistic framework to sample the future trajectory and hotspots to handle uncertainty in prediction. We perform experiments on the Epic-Kitchens-55, Epic-Kitchens-100, and EGTEA Gaze+ datasets, and show that OCT significantly outperforms state-of-the-art approaches by a large margin. Project page is available at https://stevenlsw.github.io/hoi-forecast .

• The paper introduces an Object-Centric Transformer (OCT) model that jointly predicts future hand motion and interaction hotspots from egocentric videos.
• The research leverages automated dataset generation with off-the-shelf detectors and conditional variational autoencoders to accurately model hand-object dynamics.
• Experiments demonstrate significant improvements in metrics like ADE and FDE, highlighting the model's effectiveness for augmented reality and robotics applications.

Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos

In the paper "Joint Hand Motion and Interaction Hotspots Prediction from Egocentric Videos," the authors propose a novel approach to forecast future hand-object interactions using egocentric video data. Unlike conventional video anticipation tasks, which either predict discrete future action categories or extrapolate pixel-based frame changes, this research focuses on predicting hand motion trajectories and interaction hotspots, providing a direct and efficient representation of anticipated interactions.

The method is structured around the development of a dataset created automatically from existing egocentric video datasets, Epic-Kitchens-55, Epic-Kitchens-100, and EGTEA Gaze+. Utilizing off-the-shelf hand detectors and frame homography techniques, future hand trajectories and interaction points are projected into the coordinate system of the last observed frame, allowing the researchers to circumvent the need for manual labeling of future interactions, often a labor-intensive task. This robust method enables large-scale automated data collection, which is essential for training robust models in AI.

The core of the predictive framework is the Object-Centric Transformer (OCT) model. This model employs a self-attention mechanism to reason about hand-object interactions, leveraging an encoder-decoder architecture. The encoder processes hand, object, and global scene context, while the decoder predicts future hand movements and object interaction points, incorporating Conditional Variational Autoencoders (C-VAE) to manage prediction uncertainty. This hybrid probabilistic approach allows OCT to model the inherent unpredictability in future human-object interactions comprehensively.

Results from experimentation on the aforementioned datasets demonstrate the OCT framework's superiority over existing state-of-the-art approaches. Metrics such as Average Displacement Error (ADE) and Final Displacement Error (FDE) significantly improve, underscoring the efficacy of joint trajectory and hotspot prediction tasks. Furthermore, the OCT model proves highly adaptable, offering valuable insights even when employed on tasks like action anticipation, thus highlighting its generalizable nature across related predictive tasks.

The practical implications of this research are profound, particularly for applications in augmented reality (AR) and robotics. Accurately predicting hand-object interactions could facilitate smoother human-machine collaborations, enhancing AR headset functionalities, and improving robotic responsiveness in dynamic environments. From a theoretical perspective, this work contributes a sophisticated method for modeling complex interaction dynamics using probabilistic reasoning and transformer architecture—offering potentially valuable insights for future research in video anticipation and interaction prediction.

Despite its strengths, the reliance of training label generation on pre-existing detectors introduces potential biases, which may affect label accuracy. Addressing this limitation through self-supervised learning mechanisms could offer pathways for refinement. Future work could focus on expanding this approach to other domains within egocentric video understanding, task-specific customizations for interaction-heavy settings, or further development of probabilistic models to better capture interaction complexities.

In summary, this paper presents a compelling advancement in predictive modeling within egocentric vision, leveraging automated data generation and transformer-based architectures to offer new capabilities for anticipating human-object interactions. It sets a promising trajectory toward more intuitive, responsive AI systems capable of seamless integration with real-world human activities.