PGC: Physics-Based Gaussian Cloth from a Single Pose (2503.20779v1)

Abstract: We introduce a novel approach to reconstruct simulation-ready garments with intricate appearance. Despite recent advancements, existing methods often struggle to balance the need for accurate garment reconstruction with the ability to generalize to new poses and body shapes or require large amounts of data to achieve this. In contrast, our method only requires a multi-view capture of a single static frame. We represent garments as hybrid mesh-embedded 3D Gaussian splats, where the Gaussians capture near-field shading and high-frequency details, while the mesh encodes far-field albedo and optimized reflectance parameters. We achieve novel pose generalization by exploiting the mesh from our hybrid approach, enabling physics-based simulation and surface rendering techniques, while also capturing fine details with Gaussians that accurately reconstruct garment details. Our optimized garments can be used for simulating garments on novel poses, and garment relighting. Project page: https://phys-gaussian-cloth.github.io .

Physics-Based Gaussian Cloth from a Single Pose: A Comprehensive Overview

The paper "PGC: Physics-Based Gaussian Cloth from a Single Pose" proposes a novel technique for reconstructing photorealistic, simulation-ready garments from a single static pose captured through multi-view imaging. This research addresses the limitations of existing methods that typically require extensive datasets and tend to struggle with balancing detailed garment reconstruction and generalization to new poses or body shapes. By exploiting a hybrid approach, this paper introduces a mesh-embedded 3D Gaussian splat technique, aiming to encapsulate both high-frequency details and far-field shading effects.

The authors present a sophisticated representation of garments by using 3D Gaussian splats embedded within a mesh. This dual representation is pivotal; the Gaussians effectively capture near-field shading and intricate surface details, akin to fabrics like knits or fur, while the mesh encodes the broader albedo and reflectance characteristics necessary for rendering. The method's ability to generalize across novel poses is facilitated by the hybrid use of these splats and mesh, which accommodate dynamic simulations and realistic surface rendering.

Key Contributions and Methodology

The paper introduces several technical innovations, primarily:

1. Single-Frame Garment Reconstruction: A streamlined method to reconstruct garments from a multi-view capture of a single static pose, thus eliminating the inefficiencies of traditional multi-frame tracking and data-heavy approaches.
2. Hybrid Rendering Model: Combining physics-based rendering (PBR) for low-frequency shading with Gaussian splats for high-frequency details, the method ensures realistic rendering across varying motions and lighting conditions.
3. Simulatable and Relightable Garments: The optimized garments are not only adaptable to novel poses via simulation but can also be rendered under different lighting setups, enhancing their utility in diverse virtual environments.

Results and Performance

The experimental results showcase the method's capability to generate high fidelity garments that maintain appearance consistency while accommodating novel dynamics. Quantitative analysis using metrics such as LPIPS and FSIM demonstrated the method's superiority over existing techniques like SCARF and Animatable Gaussians. Qualitatively, the garments rendered using this approach displayed enhanced realism, particularly in high-frequency detail reproduction which is critical for fabrics that feature surface intricacies.

Moreover, the computational efficiency is noteworthy. The proposed method bypasses the need for extensive preprocessing phases like multi-frame alignment, significantly reducing the computational demands and rendering times. This efficiency paves the way for real-time applications, making it viable for integration into interactive platforms such as virtual dressing rooms and telepresence systems.

Implications and Future Directions

The practical implications of this research are substantial, particularly in the fields of computer graphics, virtual reality, and augmented reality. The capability to accurately and efficiently render complex garments in simulation environments can revolutionize digital fashion, online retail, and virtual collaboration spaces. Theoretically, it opens avenues for further exploration into hybrid rendering techniques that leverage the strengths of both mesh-based and point-based representations.

For future work, advancements can focus on enhancing the mesh geometry alignment to prevent artifacts such as sagging in simulated garments. Additionally, expanding the fidelity of relighting capabilities and exploring more complex garment types with multiple layers or textures could improve the versatility of the approach. Integrating advanced machine learning models to further refine the texture and color accuracy during the albedo extraction phase promises to elevate the realism of the results.

Overall, this paper presents a substantial step forward in garment simulation and rendering, offering a balanced solution between aesthetic detail and computational efficiency. The method's implications suggest a promising trajectory for digital garment visualization in numerous advanced applications, underscoring its significance in ongoing research within computer vision and graphics.