D3D-HOI: Dynamic 3D Human-Object Interactions from Videos (2108.08420v1)

Abstract: We introduce D3D-HOI: a dataset of monocular videos with ground truth annotations of 3D object pose, shape and part motion during human-object interactions. Our dataset consists of several common articulated objects captured from diverse real-world scenes and camera viewpoints. Each manipulated object (e.g., microwave oven) is represented with a matching 3D parametric model. This data allows us to evaluate the reconstruction quality of articulated objects and establish a benchmark for this challenging task. In particular, we leverage the estimated 3D human pose for more accurate inference of the object spatial layout and dynamics. We evaluate this approach on our dataset, demonstrating that human-object relations can significantly reduce the ambiguity of articulated object reconstructions from challenging real-world videos. Code and dataset are available at https://github.com/facebookresearch/d3d-hoi.

• The paper presents a comprehensive dataset of 256 videos with detailed 3D annotations for benchmarking articulated object reconstruction.
• The proposed optimization framework leverages human-object spatial relationships to improve 3D pose and part motion estimation.
• Empirical results demonstrate that integrating human pose estimation significantly reduces reconstruction errors in dynamic scenes.

Dynamic 3D Human-Object Interactions from Videos: An Analytical Perspective

The paper presents D3D-HOI, a novel dataset designed specifically for analyzing dynamic 3D human-object interactions (HOIs). The dataset is comprised of monocular video sequences capturing real-world human interactions with articulate, everyday objects such as laptops, dishwashers, and refrigerators. Unique to D3D-HOI is its detailed annotation of 3D object pose, shape, and part motion, offering a foundational resource for evaluating and benchmarking articulated object reconstruction tasks.

Contributions and Methodology

The primary contributions of the paper include:

1. Dataset Collection: D3D-HOI consists of 256 videos filmed across diverse scenes and viewpoints, with ground truth annotations of the spatial dynamics of each interaction. Notably, 3D parametric models represent each object, providing a reference point for validating reconstruction quality.
2. Optimization-Based Reconstruction Method: The researchers propose an optimization framework leveraging human-object spatial relationships to enhance object reconstruction from RGB videos. This involves treating both humans and objects as dynamic entities, incorporating constraints such as orientation and contact terms that influence pose and motion inference.
3. Innovative Use of Human Pose Estimation: By incorporating estimated human poses, the paper demonstrates how human-object relations can mitigate ambiguities inherent in reconstructing articulated objects solely from video data.

Experimental Results and Findings

The reconstruction approach was rigorously evaluated on D3D-HOI, showing substantial improvements when human-object interaction terms were included in the optimization process. Empirical results highlighted:

• Pose Accuracy: Significant reductions in orientation error, showcasing the contribution of incorporating human interaction cues.
• Part Motion Estimation: Enhanced accuracy in estimating object part motion, particularly in objects where small movements affect human-object contact in complex ways.
• Results Robustness Without HOI Terms: A notable decline in reconstruction fidelity when human-object relational information is excluded, emphasizing the critical role of spatial relationships in driving reconstruction accuracy.

Implications and Speculation on Future Developments

The implications of this work resonate across multiple fronts:

• Practical Applications: The integration of human-object interaction models into machine learning pipelines can enhance real-world applications, from robotics to augmented reality, by enabling more nuanced and accurate modeling of dynamic scenes.
• Dataset as a Benchmarking Tool: D3D-HOI provides a comprehensive benchmark for further research in dynamic 3D reconstruction, fostering a collaborative environment to refine algorithms leveraging human-object interactions.
• Advancements in AI: This paper hints at future expansions where AI systems increasingly understand and predict nuanced human-object interactions, shifting towards more holistic interpretations of complex real-world environments.

Overall, the paper demonstrates the value of embedding human-object relations into 3D reconstruction paradigms, heralding opportunities for advanced applications in computer vision. With D3D-HOI, the authors lay a robust foundation for future explorations into dynamically reconstructed environments.