SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements (2104.07660v1)

Abstract: Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. To enable learning, the choice of representation is the key. Recent work uses neural networks to parameterize local surface elements. This approach captures locally coherent geometry and non-planar details, can deal with varying topology, and does not require registered training data. However, naively using such methods to model 3D clothed humans fails to capture fine-grained local deformations and generalizes poorly. To address this, we present three key innovations: First, we deform surface elements based on a human body model such that large-scale deformations caused by articulation are explicitly separated from topological changes and local clothing deformations. Second, we address the limitations of existing neural surface elements by regressing local geometry from local features, significantly improving the expressiveness. Third, we learn a pose embedding on a 2D parameterization space that encodes posed body geometry, improving generalization to unseen poses by reducing non-local spurious correlations. We demonstrate the efficacy of our surface representation by learning models of complex clothing from point clouds. The clothing can change topology and deviate from the topology of the body. Once learned, we can animate previously unseen motions, producing high-quality point clouds, from which we generate realistic images with neural rendering. We assess the importance of each technical contribution and show that our approach outperforms the state-of-the-art methods in terms of reconstruction accuracy and inference time. The code is available for research purposes at https://qianlim.github.io/SCALE .

• The paper introduces SCALE, a method that decouples body articulation from clothing deformations for precise 3D modeling.
• It employs local feature regression and 2D pose embedding to capture fine details and handle unseen poses.
• The approach outperforms current methods in reconstruction fidelity and speed, supporting advanced VR and animation applications.

Analysis of SCALE: A Method for Modeling Clothed Humans with Surface Codec of Articulated Local Elements

The paper "SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements" introduces a novel method aimed at enhancing the capability of 3D modeling and reconstruction of clothed human figures. This method addresses significant challenges associated with the articulation, non-rigid deformations, and varying clothing topologies inherent in modeling such complex entities.

Key Contributions

The authors present several notable innovations within the SCALE framework:

1. Articulation Decomposition: They separate large-scale deformations caused by body articulation from topological changes and local deformations in clothing. This decomposition facilitates more accurate modeling of dynamic clothing details that accompany body movements.
2. Local Feature Regression: The paper advances the parameterization of local surface elements, improving the capture of fine-grained details and deformations. The method utilizes local feature regression to predict local geometric modifications, which significantly enhances the expressiveness of the model.
3. Pose Embedding in 2D Space: A novel pose embedding strategy is introduced, which encodes body geometry in a 2D parameterization space, thereby improving the model's ability to generalize to unseen poses. This approach reduces the reliance on non-local spurious correlations, enhancing robustness and adaptability.

Comparative Performance

The efficacy of SCALE is underscored by its performance relative to state-of-the-art methodologies, specifically mesh-based and implicit surface models like CAPE and NASA, respectively. SCALE demonstrates superior accuracy in terms of reconstruction fidelity, exhibiting significant improvements in Chamfer distance and reduced normal discrepancy. The approach also achieves faster inference times, thus offering enhanced efficiency for practical applications.

The method effectively handles topological variances without the need for registered training data, which is a significant advancement over traditional mesh models. Unlike other existing methods constrained by registered mesh templates or the need for watertight inputs, SCALE comprises flexible point-based representation that supports rich geometric detail capture, even in complex garment shapes.

Implications and Future Work

Practically, the SCALE methodology can significantly advance applications in fields such as virtual reality, motion capture, and digital animation, where the accurate dynamic modeling of clothed figures is pivotal. The capacity to handle arbitrary clothing topologies and generalize across new body poses positions SCALE as a versatile tool for both existing and emerging technologies.

Theoretically, the paper contributes to the broader discourse on 3D shape representation and reconstruction by introducing an articulated approach that balances global structure coherence with fine local detailing. This dual capability is critical for evolving more nuanced modeling methodologies that can effectively simulate real-world complexities.

Future research directions could explore further enhancements in SCALE's capability for real-time applications, possibly integrating additional clothing dynamics such as texture variations or environmental effects. Improvements in computational efficiency and scalability could also expand its application reach across diverse industry settings.

Conclusion

SCALE offers a significant stride forward in the domain of clothed human modeling, expertly navigating the complexities of articulation, non-rigid deformation, and topological variety. Its contribution to improving the accuracy and efficiency of 3D shape reconstruction is commendable, presenting numerous potential applications and paving the way for future innovations in this field.