HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video (2311.18448v1)

Abstract: Since humans interact with diverse objects every day, the holistic 3D capture of these interactions is important to understand and model human behaviour. However, most existing methods for hand-object reconstruction from RGB either assume pre-scanned object templates or heavily rely on limited 3D hand-object data, restricting their ability to scale and generalize to more unconstrained interaction settings. To this end, we introduce HOLD -- the first category-agnostic method that reconstructs an articulated hand and object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can reconstruct disentangled 3D hand and object from 2D images. We also further incorporate hand-object constraints to improve hand-object poses and consequently the reconstruction quality. Our method does not rely on 3D hand-object annotations while outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings. Moreover, we qualitatively show its robustness in reconstructing from in-the-wild videos. Code: https://github.com/zc-alexfan/hold

• The paper introduces a novel compositional implicit model that jointly reconstructs 3D hands and objects without predefined templates.
• It refines initial hand and object poses using SfM and interaction constraints, significantly reducing MPJPE and Chamfer distance errors.
• The approach enhances practical applications in VR, robotics, and human-computer interaction by achieving robust performance in diverse, real-world environments.

Overview of "HOLD: Hand and Object Reconstruction from a Monocular Video"

The paper introduces a novel approach, denoted as HOLD, for the joint reconstruction of articulated hands and interacted objects from monocular video sequences. This paper addresses a significant limitation of existing methods that often require pre-scanned object templates or are constrained by limited 3D hand-object data. HOLD is designed to be the first category-agnostic method, capable of reconstructing both hands and objects without relying on predefined object categories.

Key Contributions

1. Compositional Articulated Implicit Model: The proposed method employs a compositional implicit model that enables the disentangled reconstruction of 3D hand and object surfaces from 2D images. This model is critical for overcoming the challenges of occlusion and interaction constraints.
2. Pose Initialization and Refinement: Initial hand poses are derived from an off-the-shelf hand regressor, and object poses are estimated using structure-from-motion (SfM). The method then refines these poses by leveraging interaction constraints, substantially improving reconstruction quality.
3. Interaction Constraints: By integrating hand-object interaction constraints, the model enhances the accuracy of both hand and object reconstruction compared to treating them separately. This approach allows HOLD to yield superior results over fully-supervised baselines, even in challenging in-the-wild conditions.
4. Evaluation and Generalization: The paper rigorously evaluates the method using datasets like HO3D-v3 and demonstrates its robustness in both lab and natural settings. HOLD exhibits notable generalization capabilities across various object categories and interaction scenarios, outperforming state-of-the-art methods in terms of hand pose accuracy and object reconstruction fidelity.

Experimental Results

The paper provides quantitative evaluations, indicating a significant reduction in mean-per-joint error (MPJPE) and improvements in Chamfer distance metrics when compared to baselines such as iHOI and DiffHOI. Notably, the method achieves a remarkable level of detail and realism in 3D hand and object surfaces across diverse viewpoints and lighting conditions.

Theoretical and Practical Implications

Theoretically, HOLD advances the field of computer vision by demonstrating that effective hand-object reconstruction does not require predefined templates or extensive 3D data. This category-agnostic approach suggests potential for widespread application in various domains, such as robotics, virtual reality, and ergonomic analysis.

Practically, the ability to accurately model hand-object interaction from monocular videos could enhance user interfaces and interaction models, enabling more intuitive and responsive systems. The robustness exhibited in in-the-wild scenarios also indicates potential for practical deployment in consumer-grade applications, where environmental variables are less controlled.

Future Directions

Future developments could focus on addressing the paper's limitations, such as the handling of thin or textureless objects. Integration with advancements in detector-free SfM and diffusion priors could further refine reconstruction quality and expand the method's applicability.

In conclusion, HOLD represents a significant step forward in hand-object reconstruction, providing a foundation for further exploration into more dynamic and complex interaction models without dependence on extensive pre-existing data or object templates. Its ability to generalize across various conditions positions it as a valuable contribution to the field of computer vision and artificial intelligence.