NPMs: Neural Parametric Models for 3D Deformable Shapes (2104.00702v2)

Abstract: Parametric 3D models have enabled a wide variety of tasks in computer graphics and vision, such as modeling human bodies, faces, and hands. However, the construction of these parametric models is often tedious, as it requires heavy manual tweaking, and they struggle to represent additional complexity and details such as wrinkles or clothing. To this end, we propose Neural Parametric Models (NPMs), a novel, learned alternative to traditional, parametric 3D models, which does not require hand-crafted, object-specific constraints. In particular, we learn to disentangle 4D dynamics into latent-space representations of shape and pose, leveraging the flexibility of recent developments in learned implicit functions. Crucially, once learned, our neural parametric models of shape and pose enable optimization over the learned spaces to fit to new observations, similar to the fitting of a traditional parametric model, e.g., SMPL. This enables NPMs to achieve a significantly more accurate and detailed representation of observed deformable sequences. We show that NPMs improve notably over both parametric and non-parametric state of the art in reconstruction and tracking of monocular depth sequences of clothed humans and hands. Latent-space interpolation as well as shape/pose transfer experiments further demonstrate the usefulness of NPMs. Code is publicly available at https://pablopalafox.github.io/npms.

• The paper introduces NPMs as a novel neural approach for high-fidelity 3D deformable shape reconstruction using disentangled latent spaces for shape and pose.
• It employs separate MLPs to capture implicit SDF values in canonical poses and learn deformation fields for varied poses, reducing strict data requirements.
• NPMs achieve superior performance compared to traditional models like SMPL by accurately reconstructing complex surfaces, validated with metrics like IoU and Chamfer distance.

Neural Parametric Models for 3D Deformable Shapes

The paper, "NPMs: Neural Parametric Models for 3D Deformable Shapes," introduces an innovative method for modeling 3D deformable shapes using a data-driven approach. The authors propose Neural Parametric Models (NPMs) as a novel substitute to traditional parametric models, such as SMPL, for detailed shape and pose representation in 3D computer vision and graphics tasks. This technique bypasses the cumbersome task of manually imposing object-specific constraints and instead leverages learned implicit functions to achieve high-fidelity reconstructions.

Methodology and Contributions

NPMs offer a fresh approach by disentangling shape and pose into learned latent representations. The key contributions include:

• Implicit Function-Based Model: NPMs employ two separate multi-layer perceptrons (MLPs) to learn distinct latent spaces for shape and pose. The shape space captures the implicit Signed Distance Field (SDF) values for canonical poses, while the pose space encodes deformation fields mapping canonical shapes to various posed shapes.
• Training and Data Handling: Training NPMs requires a dataset devoid of strict correspondences or part-specific constraints, which is a remarkable departure from conventional techniques. The model only necessitates that shapes are seen in their canonical and a few arbitrary poses, thereby significantly relaxing input data requirements.
• Efficient Optimization at Inference: The method allows efficient shape and pose fitting to new 3D observations, demonstrating the capability to retain detailed geometric fidelity. Optimizing over learned latent spaces enables the model to fit monocular depth sequence data, which showcases its practical applicability.

Strong Numerical Results and Implications

The paper reports substantial quantitative improvements over existing parametric and non-parametric approaches. In particular, NPMs excel in accurately reconstructing complex and detailed surfaces of objects such as clothed human bodies and hands—the quality and accuracy of these reconstructions are quantitatively validated using metrics like Intersection over Union (IoU) and Chamfer distance.

Moreover, NPMs outperform state-of-the-art models like SMPL and OFlow in tasks involving tracking and reconstruction from monocular depth sequences. Interestingly, their model allows for shape and pose transfer experiments, demonstrating its potential for interpolating between different poses and shapes—highlighted by successful experiments with human subjects and hands data.

Theoretical and Practical Implications

The theoretical novelty of NPMs lies in the flexibility and generality of learned latent spaces that effectively disentangle shape and pose. Practically, NPMs significantly expand the repertoire of deformable shape modeling, affording the synthesis of high-fidelity shapes devoid of detailed manual calibrations necessary in traditional methods.

NPMs herald a new direction for future investigations in deformable models within AI and computer vision. These could include developing domain-agnostic algorithms enabling real-time applications, enhancing model generalization capabilities across unseen domains, and reducing the learning curve for integrating different object categories.

Speculation on Future Developments

Looking ahead, the approach could evolve to encompass a broader range of dynamic objects beyond the human body, offering more versatile models capable of handling various deformations and complex topologies. Future work may also explore enhancing the robustness of latent space optimization algorithms, further integrating physical constraints for improved realism in simulations and animations.

Overall, "NPMs: Neural Parametric Models for 3D Deformable Shapes" provides a significant step forward in 3D modeling, leveraging neural architectures to offer a more nuanced and flexible solution to capturing and expressing the complexity inherent in dynamic real-world objects. Such advancements open opportunities for myriad applications in entertainment, virtual reality, and beyond.