High-Fidelity Dynamic Mesh Reconstruction from Monocular Videos

Core Concepts

DG-Mesh, a framework that reconstructs high-quality, time-consistent meshes from monocular videos, leveraging 3D Gaussian Splatting and a novel Gaussian-Mesh Anchoring procedure.

Abstract

The paper introduces DG-Mesh, a framework for reconstructing high-fidelity and time-consistent meshes from monocular videos. The key contributions are: Leveraging 3D Gaussian Splatting to construct a deformable 3D Gaussian representation of the dynamic scene, which enables efficient rendering and explicit geometry. Proposing a Gaussian-Mesh Anchoring procedure to encourage uniform distribution of the 3D Gaussian points and align them with the reconstructed mesh faces. This improves the mesh reconstruction quality. Introducing a Cycle-Consistent Deformation module to maintain the correspondence between the canonical and deformed Gaussian points, enabling tracking of mesh vertices over time. The pipeline first learns a set of canonical 3D Gaussians and a deformation network to transform them into the deformed space at each time step. A differentiable Poisson solver and Marching Cubes algorithm are then used to extract a mesh from the deformed Gaussians. The Gaussian-Mesh Anchoring adjusts the deformed Gaussians to be uniformly distributed and aligned with the mesh faces. The Cycle-Consistent Deformation projects the anchored Gaussians back to the canonical space. Experiments show that DG-Mesh outperforms previous methods in reconstructing detailed dynamic meshes, especially for challenging structures like thin bird wings. The extracted time-consistent meshes enable applications such as texture editing and ray tracing.

Stats

The paper reports the following key metrics: Chamfer Distance (CD) and Earth Mover's Distance (EMD) between the reconstructed mesh and ground truth mesh Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) of the rendered mesh images

Quotes

"Our method not only allows the mesh to have flexible topology changes but also builds the correspondence across meshes over time." "Our key observation is that the direct training of 3D Gaussians mentioned above will lead to uneven spreading of Gaussians in 3D space (as shown in Fig. 2 left). However, this is detrimental for mesh reconstruction performance due to the violation of Possion solver assumption that points should be evenly distributed." "Crucially, the dynamic mesh extracted via our method can be seamlessly integrated into a physical simulator, enabling rapid, physics-based rendering adaptable to diverse materials and lighting conditions."

Key Insights Distilled From

Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Monocular Videos

by Isabella Liu... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12379.pdf

Deeper Inquiries

How can DG-Mesh be extended to handle more complex dynamic scenes with multiple interacting objects?

DG-Mesh can be extended to handle more complex dynamic scenes with multiple interacting objects by incorporating advanced object segmentation and tracking techniques. By accurately segmenting and tracking multiple objects in the scene, DG-Mesh can establish correspondences between different objects and their respective meshes over time. This would involve developing algorithms to handle occlusions, interactions, and complex object movements. Additionally, the framework can be enhanced to support multi-object interactions, such as collisions, deformations, and transformations, by incorporating physics-based simulations or constraints into the mesh reconstruction process. By improving the object segmentation, tracking, and interaction modeling capabilities, DG-Mesh can effectively reconstruct and track meshes for complex dynamic scenes with multiple interacting objects.

What are the potential limitations of the Gaussian-Mesh Anchoring approach, and how can it be further improved to handle more challenging topological changes?

One potential limitation of the Gaussian-Mesh Anchoring approach is its reliance on the assumption of uniform distribution of Gaussians on the object's surface. In cases where the object's topology changes significantly or where there are complex surface structures, achieving uniform distribution may be challenging. This can lead to difficulties in finding accurate correspondences between the mesh faces and the Gaussians, impacting the overall mesh reconstruction quality. To address this limitation and improve the approach for handling more challenging topological changes, several enhancements can be considered: Adaptive Anchoring: Implement adaptive strategies that adjust the anchoring process based on the complexity of the object's topology. This could involve dynamically modifying the anchoring criteria or density control based on the local surface characteristics. Topology-aware Anchoring: Develop techniques that incorporate topological information into the anchoring process. By considering the object's topology during Gaussian-Mesh Anchoring, the approach can better handle topological changes and ensure more accurate correspondences. Multi-resolution Anchoring: Introduce multi-resolution anchoring techniques that adapt the anchoring process based on the scale and complexity of the object's features. This can help in capturing fine details while maintaining uniform distribution of Gaussians. By incorporating these enhancements, the Gaussian-Mesh Anchoring approach can be further improved to handle more challenging topological changes and ensure robust mesh reconstruction in complex dynamic scenes.

Can the DG-Mesh framework be adapted to work with other 3D representations beyond meshes, such as point clouds or voxels, to enable a wider range of applications?

Yes, the DG-Mesh framework can be adapted to work with other 3D representations beyond meshes, such as point clouds or voxels, to enable a wider range of applications. This adaptation would involve modifying the reconstruction and tracking processes to accommodate the specific characteristics of point clouds or voxels. For point clouds: Point Cloud Reconstruction: DG-Mesh can be extended to reconstruct surfaces directly from point clouds by developing algorithms that convert point cloud data into mesh representations. Point Cloud Tracking: The framework can incorporate point cloud tracking techniques to establish correspondences between points across different frames, enabling consistent mesh reconstruction over time. For voxels: Voxel-based Reconstruction: DG-Mesh can be modified to reconstruct surfaces from voxel grids by implementing voxel-to-mesh conversion algorithms. Voxel-based Tracking: Techniques for tracking voxel-based representations can be integrated into DG-Mesh to enable motion tracking and consistent mesh reconstruction from voxel data. By adapting DG-Mesh to work with point clouds or voxels, the framework can cater to a broader range of 3D data types and applications, including those that rely on different representations for geometry and motion capture.