H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions (1904.05349v1)

Abstract: We present a unified framework for understanding 3D hand and object interactions in raw image sequences from egocentric RGB cameras. Given a single RGB image, our model jointly estimates the 3D hand and object poses, models their interactions, and recognizes the object and action classes with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end on single images. We further merge and propagate information in the temporal domain to infer interactions between hand and object trajectories and recognize actions. The complete model takes as input a sequence of frames and outputs per-frame 3D hand and object pose predictions along with the estimates of object and action categories for the entire sequence. We demonstrate state-of-the-art performance of our algorithm even in comparison to the approaches that work on depth data and ground-truth annotations.

Overview of the H+O Framework for Egocentric 3D Hand-Object Recognition

The paper under discussion presents a novel framework named H+O for proficient recognition of 3D hand and object interactions utilizing egocentric vision from monocular RGB cameras. This model introduces a singular architecture with an impressive capacity to estimate 3D hand and object poses, model their interaction dynamics, and accurately classify both the object and activity categories through a single forward pass of a neural network.

The paper underscores the challenges of human-object interaction recognition in egocentric views, marked by complex hand-object dynamics and partial occlusions. By utilizing a fully convolutional network architecture that operates directly on raw image sequences from RGB cameras, the model demonstrates improved efficiency over previous methodologies reliant on depth data or multi-camera systems.

Key Contributions

1. Unified Framework: The H+O framework caters to four distinct tasks—3D hand pose estimation, object pose determination, object recognition, and activity classification—utilizing a shared neural architecture. This design integrates seamlessly with input from monocular RGB images, eliminating reliance on separate object detection modules or auxiliary 3D model data.
2. 3D Control Points for Pose Estimation: Central to the methodology is the unified representation of hand and object poses using 3D control points. Such representation fosters concurrent evaluation of articulated hand movements and rigid object orientations within a coherent structural framework.
3. Temporal Modeling with RNNs: An integration of temporal modeling is achieved through the use of a Long Short-Term Memory (LSTM) network, which draws on the dynamic nature of 3D hand-object motions to identify and interpret extended action sequences.
4. Interaction Modeling: A significant leap in action recognition accuracy is attributed to explicit modeling of interactions directly in 3D between hands and objects. This approach allows for nuanced interpretation of hand-object spatial configurations, surpassing standard temporal models that do not encapsulate interaction dynamics.

Evaluation and Results

The H+O framework is rigorously evaluated on the First-Person Hand Action dataset (FPHA), which encompasses extensive annotations for 3D joint locations and object interactions. Experimental outcomes reveal superior action recognition performance exceeding baseline methodologies such as those utilizing only hand or object pose data. The temporal reasoning component, particularly its ability to model hand-object interactions, underscores significant advancements in recognizing complex activities in egocentric vision environments.

Quantitative assessments establish the model’s proficiency, with observed results demonstrating a marked increase in accuracy of not only hand and object pose estimations, but also in the classification of interaction types—exemplified by nuanced grasp types and action-object task executions. The performance remains robust even when the context transitions from controlled to more cluttered, diverse real-world environments, thereby revealing significant generalization capacity.

Implications and Future Directions

The practical implications of the H+O model resonate across various domains including augmented and virtual reality, robotics, and telepresence, where real-time understanding of human-animal interactions can enhance user interactions and operational efficacy. Theoretically, the paper opens avenues for further exploration into unified representation learning across diverse visual tasks, especially in constraints posed by monocular camera systems.

Future endeavors may extend towards adaptive learning mechanisms that tune the interaction dependency model on-the-fly according to environmental context or task specificity, thereby boosting real-world applicability and efficiency. Additionally, the assimilation of two-handed interactions and human-human interaction dynamics could significantly enhance the model's breadth and functionality in contextually richer scenarios.

In conclusion, this paper delineates substantial advancements in monocular-based 3D hand-object interaction recognition, charting a course for future research endeavors and practical applications that require swift and reliable understanding of complex human-object interactions.