Accurate Joint Reconstruction of 3D Human and Object Leveraging Contact Information

Concetti Chiave

CONTHO effectively exploits human-object contact information to jointly reconstruct accurate 3D human and object meshes from a single image.

Sintesi

The paper presents CONTHO, a novel method for joint reconstruction of 3D human and object that effectively utilizes human-object contact information.

The key highlights are:

1. 3D-guided contact estimation: CONTHO first reconstructs initial 3D human and object meshes and uses them as explicit 3D guidance to estimate accurate human-object contact maps.

2. Contact-based refinement: CONTHO proposes a novel contact-based refinement Transformer (CRFormer) that selectively aggregates human and object features based on the estimated contact maps. This prevents learning of undesired human-object correlation and enables accurate 3D reconstruction.

3. State-of-the-art performance: CONTHO outperforms previous methods in both human-object contact estimation and joint 3D reconstruction of human and object.

The authors first obtain initial 3D human and object meshes using a backbone network. Then, they extract 3D vertex features from the initial meshes and feed them into the ContactFormer to estimate accurate human-object contact maps.

Finally, the CRFormer refines the initial 3D human and object meshes by selectively aggregating human and object features based on the contact maps. This contact-based refinement prevents the network from learning undesired human-object correlation, leading to accurate 3D reconstruction results.

Extensive experiments on BEHAVE and InterCap datasets demonstrate that CONTHO achieves state-of-the-art performance in both human-object contact estimation and joint 3D reconstruction of human and object, outperforming previous methods.

Statistiche

The initial 3D human mesh has 431 vertices.
The initial 3D object mesh has 64 vertices.
The image feature extracted by the backbone network has a dimension of 2048.

Citazioni

"Human-object contact serves as a strong cue to understand how humans physically interact with objects."
"Our proposed contact-based refinement prevents the learning of erroneous correlation between human and object, which enables accurate 3D reconstruction."

Approfondimenti chiave tratti da

Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer

by Hyeongjin Na... alle arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04819.pdf

Domande più approfondite

How can the proposed contact-based refinement Transformer be extended to handle more complex human-object interactions beyond simple contact, such as manipulation or tool usage?

The proposed contact-based refinement Transformer can be extended to handle more complex human-object interactions by incorporating additional features and cues into the refinement process. Here are some ways to enhance the method for handling complex interactions:

Dynamic Interaction Modeling: Introduce dynamic modeling techniques to capture interactions where the human and object are in motion or engaged in dynamic activities like tool usage or manipulation. This can involve incorporating temporal information into the refinement process to track the changing relationship between the human and object over time.

Force and Pressure Sensing: Integrate sensors or simulated force feedback data to provide information about the forces and pressures exerted during interactions. This data can be used to refine the 3D reconstruction based on the physical constraints and interactions between the human and object.

Object Deformation: Consider object deformation and shape changes that occur during interactions. By incorporating deformation models or shape adaptation algorithms, the method can better capture the varying shapes of objects as they are manipulated or used by the human.

Semantic Understanding: Incorporate semantic understanding of the scene to differentiate between different types of interactions (e.g., grasping, pushing, pulling) and adjust the refinement process accordingly. This can involve using semantic segmentation or object recognition to provide context for the interactions.

Multi-Modal Data Fusion: Combine data from multiple sources such as depth sensors, RGB cameras, and inertial sensors to provide a more comprehensive view of the interaction. By fusing information from different modalities, the method can better understand and reconstruct complex human-object interactions.

By incorporating these enhancements, the contact-based refinement Transformer can be adapted to handle a wider range of complex human-object interactions beyond simple contact, enabling more accurate and detailed 3D reconstructions in various scenarios.

How can the proposed contact-based refinement Transformer be extended to handle more complex human-object interactions beyond simple contact, such as manipulation or tool usage?

The proposed contact-based refinement Transformer can be extended to handle more complex human-object interactions by incorporating additional features and cues into the refinement process. Here are some ways to enhance the method for handling complex interactions:

Dynamic Interaction Modeling: Introduce dynamic modeling techniques to capture interactions where the human and object are in motion or engaged in dynamic activities like tool usage or manipulation. This can involve incorporating temporal information into the refinement process to track the changing relationship between the human and object over time.

Force and Pressure Sensing: Integrate sensors or simulated force feedback data to provide information about the forces and pressures exerted during interactions. This data can be used to refine the 3D reconstruction based on the physical constraints and interactions between the human and object.

Object Deformation: Consider object deformation and shape changes that occur during interactions. By incorporating deformation models or shape adaptation algorithms, the method can better capture the varying shapes of objects as they are manipulated or used by the human.

Semantic Understanding: Incorporate semantic understanding of the scene to differentiate between different types of interactions (e.g., grasping, pushing, pulling) and adjust the refinement process accordingly. This can involve using semantic segmentation or object recognition to provide context for the interactions.

Multi-Modal Data Fusion: Combine data from multiple sources such as depth sensors, RGB cameras, and inertial sensors to provide a more comprehensive view of the interaction. By fusing information from different modalities, the method can better understand and reconstruct complex human-object interactions.

By incorporating these enhancements, the contact-based refinement Transformer can be adapted to handle a wider range of complex human-object interactions beyond simple contact, enabling more accurate and detailed 3D reconstructions in various scenarios.

What are the potential applications of the accurate joint 3D reconstruction of human and object beyond AR/VR and robotics, and how can the method be further improved to address those applications?

The accurate joint 3D reconstruction of human and object has various potential applications beyond AR/VR and robotics. Some of these applications include:

Medical Imaging: The method can be used for precise anatomical modeling and surgical planning, allowing for personalized medical treatments and simulations.

Forensic Analysis: Accurate 3D reconstructions can aid in crime scene investigations, accident reconstructions, and forensic analysis by providing detailed spatial information.

Cultural Heritage Preservation: The method can be applied to digitize and preserve cultural artifacts, historical sites, and artworks in 3D for documentation and conservation purposes.

Retail and E-Commerce: Enhanced 3D reconstructions can improve virtual try-on experiences, product visualization, and online shopping by providing realistic representations of products and human interactions.

To further improve the method for these applications, the following enhancements can be considered:

Fine-grained Interaction Modeling: Develop algorithms to capture subtle interactions between humans and objects, such as delicate object manipulation or intricate hand-object interactions.

Real-time Processing: Optimize the method for real-time processing to enable applications that require immediate feedback or interaction, such as virtual fitting rooms or interactive simulations.

Scalability and Generalization: Enhance the method's scalability and generalization capabilities to handle diverse scenarios, objects, and human interactions across different domains and applications.

Privacy and Security: Implement privacy-preserving techniques to ensure the confidentiality of sensitive data captured during 3D reconstructions, especially in applications like medical imaging or forensic analysis.

By addressing these aspects, the accurate joint 3D reconstruction method can be tailored to a wider range of applications beyond AR/VR and robotics, offering valuable insights and solutions in various fields.

The paper focuses on single-view reconstruction, but how could the method be adapted to leverage multi-view or temporal information to further improve the 3D reconstruction accuracy?

Adapting the method to leverage multi-view or temporal information can significantly enhance 3D reconstruction accuracy by providing additional perspectives and context. Here are some strategies to incorporate multi-view or temporal information into the reconstruction process:

Multi-View Fusion: Integrate data from multiple views or cameras to create a more comprehensive 3D representation. By fusing information from different viewpoints, the method can improve depth estimation, object localization, and scene understanding.

Stereo Vision: Utilize stereo vision techniques to extract depth information from stereo image pairs. By incorporating stereo matching algorithms, the method can enhance depth perception and object reconstruction accuracy.

Temporal Consistency: Leverage temporal information from video sequences to track object movements and human interactions over time. By considering the temporal consistency of object poses and human actions, the method can produce more coherent and accurate 3D reconstructions.

3D Motion Estimation: Integrate 3D motion estimation algorithms to capture dynamic interactions and movements in the scene. By analyzing the motion trajectories of objects and humans, the method can improve the spatial alignment and reconstruction quality.

Dynamic Scene Modeling: Develop algorithms to handle dynamic scenes and objects that change over time. By incorporating dynamic scene modeling techniques, the method can adapt to varying environments and interactions for more accurate reconstructions.

By incorporating these strategies, the method can leverage multi-view or temporal information to enhance 3D reconstruction accuracy, robustness, and realism in various applications.