NASA: Neural Articulated Shape Approximation (1912.03207v5)

Abstract: Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. This paper introduces neural articulated shape approximation (NASA), an alternative framework that enables efficient representation of articulated deformable objects using neural indicator functions that are conditioned on pose. Occupancy testing using NASA is straightforward, circumventing the complexity of meshes and the issue of water-tightness. We demonstrate the effectiveness of NASA for 3D tracking applications, and discuss other potential extensions.

• The paper introduces NASA, a neural occupancy-based method that approximates articulated shapes using implicit functions conditioned on pose.
• It employs three modeling approaches—Unstructured, Piecewise Rigid, and Piecewise Deformable—with the latter achieving superior pose generalization and an F-score improvement of +49% on DFaust.
• The approach simplifies complex 3D tracking and inverse graphics tasks while paving the way for advanced applications in computer vision and augmented reality.

An Expert Analysis of Neural Articulated Shape Approximation (NASA)

The paper "Neural Articulated Shape Approximation" (NASA) introduces a novel approach for the representation of articulated deformable objects using neural occupancy functions conditioned on pose. This methodology streamlines the complexity typically associated with polygonal mesh representations and their reliance on skinning techniques for object deformation. Below, we delve into the core components of the paper, its empirical results, and the broader implications of this work.

Framework and Contributions

The primary contribution of this paper is the proposal of NASA, which utilizes a neural network-based model to determine the occupancy of articulated objects. Traditional representations often face difficulties with issues like mesh water-tightness and computational overhead in occupancy queries. NASA circumvents these limitations by directly utilizing neural implicit functions that remain continuous and differentiable, thus enhancing the utility in inverse graphics and other applications requiring differentiable 3D geometry representations.

The paper introduces three different modeling approaches to articulate shapes:

1. Unstructured Model (U): A baseline method utilizing direct concatenation of pose parameters, which tends to perform inadequately due to its inability to effectively generalize across diverse poses.
2. Piecewise Rigid Model (R): This model assumes objects can be divided into a collection of rigid components. It improves the representation over the unstructured model by encoding the geometry separately in local coordinate frames.
3. Piecewise Deformable Model (D): Extending the rigid model, this approach allows for localized non-rigid deformations, providing significant boosts in capturing pose-dependent deformations and facilitating high generalization capabilities.

Empirical Evaluation

The application of NASA was rigorously tested on datasets like AMASS/DFaust and AMASS/Transitions. Key performance metrics include mean Intersection over Union (IoU), Chamfer L1, and the F-score. The piecewise deformable model consistently outperforms other variants with a notable improvement in generalization to unseen poses (e.g., achieving an F-score improvement of +49% over the unstructured model in tests on the DFaust dataset). This demonstrated the inadequacy of traditional unstructured models and highlighted the efficiency introduced by exploiting localized geometry structures.

Moreover, the paper illustrated NASA's effectiveness in 3D tracking, a notoriously complex task when using mesh-based models due to non-trivial implementation and optimization complexities. NASA's representation of articulated objects vastly simplifies this by avoiding the need for pre-computed spatial acceleration data structures.

Implications and Future Work

The theoretical and practical implications of NASA are substantial, marking a shift towards using neural occupancy functions in simulating deformable articulated structures. This approach not only advances our capabilities in realistic geometry tracking but also paves the way for more complex applications in computer vision, graphics, augmented and virtual reality, and beyond.

Despite the advantages, the implementation reveals certain limitations, such as the necessity of pre-determined part-decompositions and initialization for the pose parameters. Addressing these constraints would involve further development in automated part detection and pose estimation from raw sensor data, which is an avenue open for future research.

NASA's methodology holds promise for expansion beyond singular articulated objects to incorporate identity parameters, allowing for more comprehensive models that capture both pose and shape variability across different subjects. Additionally, integrating recent advancements in neural implicit function modeling can enhance the capturing of high-frequency geometric details, thus refining the model's applicability.

In conclusion, NASA offers a robust and efficient alternative to traditional mesh-based deformation models and introduces a scalable solution for various 3D modeling and tracking challenges within artificial intelligence and computer graphics domains. Its potential applications and expanding research directions continue to emphasize the critical role of refined neural representations in modern computational tasks.