PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations (2404.04421v2)

Abstract: Modeling and rendering photorealistic avatars is of crucial importance in many applications. Existing methods that build a 3D avatar from visual observations, however, struggle to reconstruct clothed humans. We introduce PhysAvatar, a novel framework that combines inverse rendering with inverse physics to automatically estimate the shape and appearance of a human from multi-view video data along with the physical parameters of the fabric of their clothes. For this purpose, we adopt a mesh-aligned 4D Gaussian technique for spatio-temporal mesh tracking as well as a physically based inverse renderer to estimate the intrinsic material properties. PhysAvatar integrates a physics simulator to estimate the physical parameters of the garments using gradient-based optimization in a principled manner. These novel capabilities enable PhysAvatar to create high-quality novel-view renderings of avatars dressed in loose-fitting clothes under motions and lighting conditions not seen in the training data. This marks a significant advancement towards modeling photorealistic digital humans using physically based inverse rendering with physics in the loop. Our project website is at: https://qingqing-zhao.github.io/PhysAvatar

• The paper introduces a novel framework that fuses inverse rendering with physics-based cloth simulation for photorealistic 3D avatar reconstruction.
• It employs 4D Gaussian-based mesh tracking and gradient-based material estimation to capture dynamic garment behavior under various motions.
• The approach advances digital human modeling in VR, gaming, and e-commerce by ensuring realistic avatar animations and material properties.

Comprehensive Study of PhysAvatar: Bridging 3D Avatars and Physics for Photorealistic Rendering

The advent of photorealistic digital avatars has become increasingly critical in various sectors, including virtual reality, gaming, and e-commerce. The paper introduces a novel framework, PhysAvatar, aiming to reconstruct and render 3D avatars from multi-view video data by integrating physics-based modeling for cloth dynamics, offering an advancement in the creation of digital humans.

Overview of PhysAvatar

PhysAvatar distinguishes itself by addressing the intricacies involved in accurately capturing the motion and appearance of clothed individuals. Traditional methods often fail to deliver realistic renderings, especially for garments that exhibit significant loose-fitting behavior. PhysAvatar tackles this challenge through a unique combination of inverse rendering and inverse physics. This technique not only estimates the shape and appearance of the avatar but also accurately captures the physical fabric properties of the garments.

Key Contributions

• Physics-integrated Inverse Rendering: At its core, PhysAvatar enhances inverse rendering approaches by incorporating physical dynamics of garments, thereby enabling more realistic animations under novel views and motions not present in the training dataset.
• Mesh Tracking with 4D Gaussians: The paper introduces the use of 4D Gaussians for mesh tracking over video sequences, ensuring robust surface point correspondences crucial for subsequent simulation steps.
• Physics-based Material Estimation: A critical aspect of PhysAvatar is the estimation of material properties (e.g., stiffness and density) using gradient-based optimization in conjunction with a physics simulator. This approach facilitates realistic garment behavior prediction across different motions.
• Physically Based Appearance Estimation: Leveraging a physics-based inverse rendering framework, PhysAvatar is capable of rendering avatars under novel lighting conditions, significantly enhancing photorealism.

Theoretical Underpinnings and Practical Applications

The technical merit of PhysAvatar lies in its methodological approach, integrating advanced mesh tracking, physics-based dynamic modeling, and rendering techniques. This integration not only exemplifies a significant technical feat but also raises the bar for how digital humans can be modeled and rendered with high fidelity. Practically, the framework can revolutionize character animation in films and video games, telepresence in virtual meetings, and the digital try-on experience in e-commerce, pushing the boundaries of digital interaction.

Future Outlook and Research Directions

While PhysAvatar represents a substantial leap forward, the research opens numerous avenues for further exploration. A direct extension could involve automating the segmentation and parameter estimation processes to accommodate a broader range of garments and motions. Optimizing computation time and resource efficiency remains crucial for wider adoption, especially for real-time applications.

Concluding Remarks

PhysAvatar sets a new standard in the reconstruction and rendering of photorealistic avatars, seamlessly combining the realms of computer vision, physics, and graphics. By bridging the gap between visual observations and physical behavior, it achieves remarkable realism in digital human modeling. As the field advances, the integration of such physics-aware models in generative AI could redefine the future of digital content creation, offering unprecedented levels of realism and interactivity.