S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling

Abstract: Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation. As there are exponentially many variations of humans with different shape, pose and clothing, it is critical to develop methods that can automatically reconstruct and animate humans at scale from real world data. Towards this goal, we represent the pedestrian's shape, pose and skinning weights as neural implicit functions that are directly learned from data. This representation enables us to handle a wide variety of different pedestrian shapes and poses without explicitly fitting a human parametric body model, allowing us to handle a wider range of human geometries and topologies. We demonstrate the effectiveness of our approach on various datasets and show that our reconstructions outperform existing state-of-the-art methods. Furthermore, our re-animation experiments show that we can generate 3D human animations at scale from a single RGB image (and/or an optional LiDAR sweep) as input.

Overview of Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling

The paper "S$^3$: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling" introduces a novel framework for automated 3D human modeling using neural implicit functions, addressing the intricate challenges associated with reconstructing and animating humans in virtual environments from real-world data. This method is particularly significant for applications in areas like virtual reality and robotics testing, where accurate and scalable human modeling is essential.

Methodological Approach

The proposed framework leverages neural implicit functions to jointly estimate the shape, skeleton, and skinning weights of pedestrians, utilizing data from RGB images and LiDAR sweeps. This approach diverges from traditional parametric body models by formulating pedestrian shapes and poses as continuous multidimensional fields, enabling the representation of a broad spectrum of human geometries without explicit body model fitting.

Key components of the method include:

• Multi-modal Feature Representation: The system processes sparse LiDAR point clouds and RGB images using separate convolutional networks to extract volumetric and image features.
• Point Feature Encoding: The framework synthesizes information from these multi-modal features and the 3D spatial coordinates of the points to create a comprehensive feature encoding.
• Neural S$^3$ Field: Three distinct neural networks predict occupancy probabilities, joint positions, and skinning weights, forming a high-dimensional vector field that encapsulates the pedestrian's geometry and articulation.

The inference process involves sampling points across the 3D space to construct a continuous probability field, which is then used for extracting explicit 3D representations. The model outputs are subjected to post-processing to generate animated models that can be adapted to new poses with motion capture data or artist-defined animations.

Experimental Results

Quantitative and qualitative evaluations demonstrate superior performance of this approach over state-of-the-art methods. Notably, the authors report improvements in shape reconstruction quality on both synthetic datasets and real-world datasets gathered from urban environments, with particular efficacy in handling various human poses and reanimations from novel viewpoints. The paper provides detailed metrics such as Chamfer Distance and Point-to-Surface error, which are indicative of the reconstruction precision.

The authors perform extensive comparisons with existing models, including Pixel-Aligned Implicit Function (PIFu) and other parametric approaches, illustrating the advantages of their method in scaling, efficiency, and fidelity to real-world human anatomy.

Implications and Future Directions

This research offers significant practical implications for industries involving virtual reality and simulations, where generating realistic human models at scale is increasingly relevant. The theoretically elegant formulation using neural implicit fields suggests promising avenues for further advancements in 3D modeling capabilities, particularly in handling complex articulation and clothing geometries.

Future work could explore extensions of this framework to accommodate dynamic interactions within scenes, potentially incorporating temporal learning mechanisms or more sophisticated sensory fusion techniques to improve robustness in heterogeneous environments. Additionally, enhancing the integration with motion capture data could refine animation accuracy and the ease with which virtual retargeting can adapt to diverse human activities.

In conclusion, the paper presents a sophisticated approach to 3D human modeling, with robust results and potential broad applicability in fields where precise and scalable human digitization is required.