HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation (2504.06210v1)

Abstract: We present Hierarchical Motion Representation (HiMoR), a novel deformation representation for 3D Gaussian primitives capable of achieving high-quality monocular dynamic 3D reconstruction. The insight behind HiMoR is that motions in everyday scenes can be decomposed into coarser motions that serve as the foundation for finer details. Using a tree structure, HiMoR's nodes represent different levels of motion detail, with shallower nodes modeling coarse motion for temporal smoothness and deeper nodes capturing finer motion. Additionally, our model uses a few shared motion bases to represent motions of different sets of nodes, aligning with the assumption that motion tends to be smooth and simple. This motion representation design provides Gaussians with a more structured deformation, maximizing the use of temporal relationships to tackle the challenging task of monocular dynamic 3D reconstruction. We also propose using a more reliable perceptual metric as an alternative, given that pixel-level metrics for evaluating monocular dynamic 3D reconstruction can sometimes fail to accurately reflect the true quality of reconstruction. Extensive experiments demonstrate our method's efficacy in achieving superior novel view synthesis from challenging monocular videos with complex motions.

Overview of HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation

The paper "HiMoR: Monocular Deformable Gaussian Reconstruction with Hierarchical Motion Representation" introduces a novel approach to the challenging problem of monocular dynamic 3D scene reconstruction. This work builds upon the limitations of existing 3D Gaussian Splatting (3DGS) methods, which struggle to achieve high-quality dynamic 3D reconstruction from monocular video inputs due to the inherent lack of multi-view constraints and the complexity of capturing fine-grain motion details.

Core Contributions and Methodology

This paper puts forward Hierarchical Motion Representation (HiMoR), which innovatively utilizes a tree structure to delineate motion within everyday scenes. The nodes in this structure represent different granularities of motion, where shallower nodes approximate coarse global motions and deeper nodes capture minute movement details. This hierarchical scheme exploits the idea that movement can often be broken down into simpler underlying motions, making the learning process more efficient and the resulting representations more expressive.

A distinctive feature of HiMoR is its use of shared motion bases across different nodes. This leverages the assumption that motion patterns are often smooth and share similarities across different parts of the scene, thus allowing a structured deformation approach that is computationally effective and maintains temporal consistency.

In addition to the motion representation, the authors propose using perceptual metrics for evaluating reconstruction quality. The traditional pixel-level metrics such as PSNR and SSIM can be misleading, particularly due to misalignments induced by depth ambiguities and imperfect camera parameter estimations. The perceptual metrics are more aligned with human perception and thus offer a more reliable evaluation criterion.

Experimental Results and Analysis

The experimental evaluation on standard benchmarks indicates the superiority of HiMoR in achieving state-of-the-art performance in novel view synthesis, with significant improvements in rendering quality from monocular videos depicting complex dynamic motions. The hierarchical representation successfully maintains spatio-temporal smoothness while capturing detailed scene dynamics, substantiated by perceptual metrics and qualitative visualizations.

Importantly, the paper acknowledges the critical role of initializations, employing pre-trained models for 2D tracking and depth estimation to initialize node positions and motion sequences, which is crucial given the ill-posed nature of the task at hand.

Furthermore, the paper discusses a node densification strategy to progressively model motion over regions initially sparse in node coverage. This is accomplished by evaluating the coverage of nodes relative to the Gaussians and refining their distribution to ensure even and adequate spatial representation of motion.

Implications and Future Directions

The proposed HiMoR framework advances the state of monocular dynamic 3D reconstruction by targeting the intricate challenge of motion decomposition and representation at varying scales. The theoretical implications suggest a potential paradigm shift towards more hierarchical and shared representations in dynamic scene modeling.

Practically, this work fosters enhancements in applications involving virtual reality, video production, and personal experience capturing, where realistic and dynamic scene reconstruction is paramount. The hierarchical motion model is particularly promising for scenarios requiring fine control over motion representation, potentially impacting asset creation and animation in virtual settings.

Future developments could focus on extending the hierarchical framework to incorporate adaptive canonical spaces or separate branches for handling newly introduced dynamic elements, which would address the current challenges in representing entirely new motions or unobserved scene components. Exploring adaptive hierarchical structures could provide further robustness and adaptability across diverse scene complexities and motion types.