Human-Aware Object Placement for Visual Environment Reconstruction (2203.03609v2)

Abstract: Humans are in constant contact with the world as they move through it and interact with it. This contact is a vital source of information for understanding 3D humans, 3D scenes, and the interactions between them. In fact, we demonstrate that these human-scene interactions (HSIs) can be leveraged to improve the 3D reconstruction of a scene from a monocular RGB video. Our key idea is that, as a person moves through a scene and interacts with it, we accumulate HSIs across multiple input images, and optimize the 3D scene to reconstruct a consistent, physically plausible and functional 3D scene layout. Our optimization-based approach exploits three types of HSI constraints: (1) humans that move in a scene are occluded or occlude objects, thus, defining the depth ordering of the objects, (2) humans move through free space and do not interpenetrate objects, (3) when humans and objects are in contact, the contact surfaces occupy the same place in space. Using these constraints in an optimization formulation across all observations, we significantly improve the 3D scene layout reconstruction. Furthermore, we show that our scene reconstruction can be used to refine the initial 3D human pose and shape (HPS) estimation. We evaluate the 3D scene layout reconstruction and HPS estimation qualitatively and quantitatively using the PROX and PiGraphs datasets. The code and data are available for research purposes at https://mover.is.tue.mpg.de/.

Human-Aware Object Placement for Visual Environment Reconstruction

This paper presents a novel approach to monocular 3D scene reconstruction, emphasizing human-aware interactions. Traditional methods in this domain often overlook the significance of human interactions, leading to physically implausible reconstructions. This work introduces a framework called black, which accumulates Human-Scene Interaction (HSI) data across multiple frames to optimize 3D scene layouts, ensuring physical plausibility and improving human-scene contact reasoning.

Methodology

The approach utilizes three distinct constraints derived from human movements: depth ordering, collision avoidance, and contact coherence. By considering these interactions, the method refines initial scene layouts obtained from existing monocular 3D reconstruction models. The black framework integrates several critical components:

1. Depth Order Constraint: This relies on human-object occlusion to infer the relative depth of objects. If a human occludes an object, the far side of the person limits how close the object can be. Conversely, if the object occludes the human, this defines a limit for the object's proximity to the viewer.
2. Collision Constraint: Implements a signed distance field (SDF) mechanism to penalize interpenetration between object and human meshes. This constraint ensures that humans and objects occupy separate spaces unless explicitly intended to contact.
3. Contact Constraint: This focuses on aligning the human body parts with the object parts they contact, leveraging vertex correspondence checks. Unlike previous methods, this model allows humans to interact with multiple objects simultaneously.

The optimization of these constraints is conducted using an Adam optimizer, iterating over scale, translation, and rotation parameters of the 3D objects, bringing them into a coherent scene layout.

Outcomes

The methodology was tested quantitatively and qualitatively on the PROX and datasets. The results indicate significant improvements in 3D scene layout accuracy over existing methods, such as HolisticMesh and Total3D. The refined scenes also enabled improved human pose and shape estimations, demonstrating the synergistic potential of integrating human interactions into scene reconstruction processes.

Implications and Future Directions

The paper suggests several practical and theoretical implications:

• Synergistic Reconstruction: The research encourages a paradigm shift where human interactions are integrally considered in scene reconstruction, potentially leading to more natural and usable models in synthetic environments such as virtual reality (VR) and augmented reality (AR).
• Extended Dynamics: Future research could explore dynamic scene reconstruction, accounting for movable objects and evolving configurations as humans interact with their environments.
• Advanced Geometric Representations: Adopting more flexible object representations might provide additional benefits, allowing for detailed shape optimization alongside scene layout modifications.

While the paper primarily targets static environments with fixed cameras, future endeavors might address dynamic scenarios with moving cameras and participants, broadening the applicability of this approach.

Overall, the framework proposed in this paper marks a significant step forward in the quest for realistic 3D scene reconstruction, laying the groundwork for more immersive and interactive virtual environments.