Garment4D: Garment Reconstruction from Point Cloud Sequences (2112.04159v1)

Abstract: Learning to reconstruct 3D garments is important for dressing 3D human bodies of different shapes in different poses. Previous works typically rely on 2D images as input, which however suffer from the scale and pose ambiguities. To circumvent the problems caused by 2D images, we propose a principled framework, Garment4D, that uses 3D point cloud sequences of dressed humans for garment reconstruction. Garment4D has three dedicated steps: sequential garments registration, canonical garment estimation, and posed garment reconstruction. The main challenges are two-fold: 1) effective 3D feature learning for fine details, and 2) capture of garment dynamics caused by the interaction between garments and the human body, especially for loose garments like skirts. To unravel these problems, we introduce a novel Proposal-Guided Hierarchical Feature Network and Iterative Graph Convolution Network, which integrate both high-level semantic features and low-level geometric features for fine details reconstruction. Furthermore, we propose a Temporal Transformer for smooth garment motions capture. Unlike non-parametric methods, the reconstructed garment meshes by our method are separable from the human body and have strong interpretability, which is desirable for downstream tasks. As the first attempt at this task, high-quality reconstruction results are qualitatively and quantitatively illustrated through extensive experiments. Codes are available at https://github.com/hongfz16/Garment4D.

Overview of Garment4D: Garment Reconstruction from Point Cloud Sequences

The paper "Garment4D: Garment Reconstruction from Point Cloud Sequences" presents a novel framework for reconstructing 3D garments from point cloud sequences. The approach addresses key limitations of prior methods, notably those relying on 2D images, which pose challenges such as scale and pose ambiguities. The proposed technique exploits 3D point cloud data to facilitate more robust garment reconstruction, separating garments from the human form and allowing fine control over garment topology.

Key Contributions

The research introduces a structured process for garment reconstruction comprising three primary tasks: sequential garment registration, canonical garment estimation, and posed garment reconstruction. Central to this method is the Proposal-Guided Hierarchical Feature Network and Iterative Graph Convolution Network, alongside a Temporal Transformer for managing garment dynamics. These elements collectively enhance the learning of both high-level and low-level features, essential for accurate reconstruction of fine garment details.

The paper identifies several technological challenges, including capturing the dynamic interaction between garments and the human body and overcoming the unstructured nature of point clouds. The authors address these challenges through innovative modules like the Interpolated Linear Blend Skinning and displacement prediction strategies. The proposed method is unique in effectively handling loose garments with complex dynamics, such as skirts, through tailored garment models and feature extraction techniques.

Methodology

1. Garment Registration: This step involves re-meshing garment sequences to a consistent topology using a template mesh, catering to garments' variability and enabling uniform analysis across sequences.
2. Canonical Garment Estimation: The authors leverage point-wise semantic segmentation and PCA coefficients to predict and reconstruct canonical garment meshes, representing a foundational step in detaching garments from the body structure.
3. Posed Garment Reconstruction: The framework utilizes the Proposal-Guided Hierarchical Feature Network to gather detailed geometric data and human surface encodings, refining the garment's relationship with the underlying human form. The Iterative GCN refines the garment's displacement iteratively to ensure fidelity to real-world dynamics.

Results and Implications

Quantitative experiments show that Garment4D outperforms existing methods like Multi-Garment Net, particularly when reconstructing garments that are not homotopic to the body. The paper outlines significant enhancements in reconstruction accuracy and temporal smoothness, essential for dynamic garment simulations. The system's robustness to incomplete data and segmentation errors illustrates its practical viability and potential application in diverse scenarios, from virtual try-ons to animations in AR/VR settings.

Future Directions

The implications of Garment4D are far-reaching for fields that involve virtual representations of clothing, including fashion, entertainment, and retail. As 3D sensors become increasingly accessible and capable, the approach's reliance on point cloud data aligns well with evolving technological capabilities. Future research could explore extending this method to simultaneous multi-layer garment reconstructions and adapting it for real-time applications. Moreover, the integration of material properties into garment dynamics offers a promising avenue for enhancing realism in virtual environments.

In summary, Garment4D represents a significant advancement in garment reconstruction technology, providing a robust framework that outperforms current methods in accuracy and flexibility. This work sets the stage for more sophisticated garment modeling techniques in AI-driven virtual environments.