COAP: Compositional Articulated Occupancy of People (2204.06184v1)

Abstract: We present a novel neural implicit representation for articulated human bodies. Compared to explicit template meshes, neural implicit body representations provide an efficient mechanism for modeling interactions with the environment, which is essential for human motion reconstruction and synthesis in 3D scenes. However, existing neural implicit bodies suffer from either poor generalization on highly articulated poses or slow inference time. In this work, we observe that prior knowledge about the human body's shape and kinematic structure can be leveraged to improve generalization and efficiency. We decompose the full-body geometry into local body parts and employ a part-aware encoder-decoder architecture to learn neural articulated occupancy that models complex deformations locally. Our local shape encoder represents the body deformation of not only the corresponding body part but also the neighboring body parts. The decoder incorporates the geometric constraints of local body shape which significantly improves pose generalization. We demonstrate that our model is suitable for resolving self-intersections and collisions with 3D environments. Quantitative and qualitative experiments show that our method largely outperforms existing solutions in terms of both efficiency and accuracy. The code and models are available at https://neuralbodies.github.io/COAP/index.html

• The paper introduces a hybrid approach that integrates geometric priors and localized encodings to efficiently reconstruct articulated human bodies.
• It employs a part-aware encoder-decoder architecture with PointNet and MLPs to capture local shape deformations and enhance generalization.
• Experimental results demonstrate superior IoU scores and inference speed, making COAP viable for real-time VR/AR and human-computer interaction applications.

An Essay on "COAP: Compositional Articulated Occupancy of People"

The paper under consideration introduces COAP, a novel approach for representing articulated human bodies using neural implicit models. The core innovation lies in decomposing a human body into articulated body parts and utilizing a part-aware encoder-decoder architecture to learn neural articulated occupancy, thereby modeling complex deformations locally. This compositional strategy addresses prevalent challenges in existing models, specifically poor generalization to highly articulated poses and slow inference times.

Methodology and Key Innovations

The authors of this paper propose a hybrid approach that combines traditional parametric body modeling with modern neural implicit representations. The methodology is built upon two critical insights:

• Localized Shape Encoding: Recognizing that capturing merely the entire surface area could lead to overfitting, the authors introduce a localized encoding strategy. By focusing on local body parts and their immediate neighbors in the kinematic chain, the method reduces the risk of spurious correlations and enhances generalization capability to novel poses.
• Geometric Prior Integration: Leveraging known human body models (such as SMPL), COAP integrates simple geometric primitives (3D bounding boxes) as priors. These priors guide the neural networks in appropriately allocating modeling capacity, simplifying the learning process and improving pose generalization.

The technical depth of the paper is evidenced by the elaboration on the localized encoder-decoder architecture, which includes shared PointNet for encoding local shape codes and MLP-based decoder architectures for occupancy prediction.

Experimental Validation

Quantitative and qualitative evaluations clearly demonstrate that COAP outperforms existing models such as SNARF, LEAP, and Neural-GIF in both accuracy and efficiency. Notably, COAP achieves superior results in terms of intersection-over-union (IoU) on datasets like PosePrior and DFaust, highlighting its ability to generalize across a wide range of human shapes and highly articulated poses.

Furthermore, COAP shows significant improvements in computational efficiency, with inference times more than 10 times faster than SNARF while maintaining robustness in performance. This efficiency makes COAP not just theoretically appealing but also practically viable for applications requiring real-time performance, such as VR/AR interfaces and advanced human-computer interaction systems.

Addressing Self-Intersections and Scene Interactions

The challenges of self-intersections and environmental collisions in traditional mesh-based methods have been longstanding. COAP addresses these problems with a straightforward optimization algorithm that effectively unravels self-intersections and mitigates interactions with 3D environments. The experiment on the PROX dataset reveals COAP's practicality in real-world scenarios by reducing penetrations with the scene geometry and achieving physically plausible reconstructions.

Implications and Future Directions

The introduction of COAP advances the theoretical understanding of human body modeling by integrating compositional neural occupancy fields with geometric priors. This framework not only enhances the accuracy of 3D human reconstructions but also sets a new benchmark for efficiency in neural implicit methods.

Looking forward, COAP opens up potential research avenues such as enhancing generalization to extreme body shapes, integrating clothing into the model, and exploring the deployment of COAP in 3D human estimation tasks. The method promises substantial impact across domains leveraging human body modeling, including animation, robotics, and virtual reality. As the neural implicit paradigm continues to evolve, COAP represents a foundational step towards more dynamic and adaptable representations of articulated objects.