Abstract
Monocular dynamic reconstruction is a challenging and long-standing vision problem due to the highly ill-posed nature of the task. Existing approaches are limited in that they either depend on templates, are effective only in quasi-static scenes, or fail to model 3D motion explicitly. In this work, we introduce a method capable of reconstructing generic dynamic scenes, featuring explicit, full-sequence-long 3D motion, from casually captured monocular videos. We tackle the under-constrained nature of the problem with two key insights: First, we exploit the low-dimensional structure of 3D motion by representing scene motion with a compact set of SE3 motion bases. Each point's motion is expressed as a linear combination of these bases, facilitating soft decomposition of the scene into multiple rigidly-moving groups. Second, we utilize a comprehensive set of data-driven priors, including monocular depth maps and long-range 2D tracks, and devise a method to effectively consolidate these noisy supervisory signals, resulting in a globally consistent representation of the dynamic scene. Experiments show that our method achieves state-of-the-art performance for both long-range 3D/2D motion estimation and novel view synthesis on dynamic scenes. Project Page: https://shape-of-motion.github.io/
Overview
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This paper introduces a method for reconstructing dynamic 3D scenes from single monocular videos, addressing limitations such as reliance on templates and lack of explicit 3D motion modeling.
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The approach involves representing scene motion with $SE$ motion bases and consolidating data-driven priors from monocular depth maps and long-range 2D tracks.
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Evaluations on real-world and synthetic datasets demonstrate significant improvements in 3D and 2D tracking accuracy, as well as novel view synthesis performance.
Shape of Motion: 4D Reconstruction from a Single Video
The paper "Shape of Motion: 4D Reconstruction from a Single Video" by Wang et al., introduces an innovative approach to the challenging problem of reconstructing dynamic 3D scenes captured from monocular videos. This work addresses key limitations of existing methods, such as reliance on templates, effectiveness restricted to quasi-static scenes, and lack of explicit 3D motion modeling. The proposed method achieves state-of-the-art performance in long-range 3D/2D motion estimation and novel view synthesis.
Core Contributions
The authors' method hinges on two primary insights to tackle the under-constrained problem of monocular dynamic reconstruction:
- Exploitation of Low-Dimensional Structure in 3D Motion: By representing scene motion with a compact set of $SE$ motion bases, the method encapsulates complex scene dynamics as a soft composition of multiple rigid movements. Each point's motion is expressed as a linear combination of these bases, enabling a structured and manageable decomposition of the scene.
- Consolidation of Data-Driven Priors: The method integrates monocular depth maps and long-range 2D tracks to consolidate noisy supervisory signals into a globally consistent scene representation. This integration leverages the complementary strengths of various data-driven priors while managing their inherent noise.
Methodology
Dynamic Scene Representation
The scene is represented using 3D Gaussians that parameterize geometry and appearance. The method models each 3D Gaussian's motion across video frames using rigid transformations derived from $SE(3)$ motion bases. Explicit full-length 3D motion trajectories are obtained by fitting these 3D Gaussians to the observed data.
Motion Parameterization
The compact motion representation utilizes basis trajectories shared among all scene elements. Each Gaussian's pose at a given time is determined by a linear combination of these bases, weighted by coefficients specific to each Gaussian. This formulation imposes a low-dimensional structure, thus regularizing and simplifying the optimization process.
Optimization Strategy
Initialization involves fitting initial noisy observations and k-means clustering of velocity vectors to refine the motion bases. The optimization process incorporates both reconstruction losses (for RGB, depth, and masks) and motion supervision losses (for 2D tracks and 3D motion consistency). The iterative refinement ensures the alignment of rendered outputs with the input supervisory signals, thereby achieving a coherent 4D reconstruction.
Experiments and Results
The method's efficacy is demonstrated through exhaustive evaluations on real-world (iPhone dataset) and synthetic (Kubric) datasets. On the iPhone dataset, it surpasses existing methods in 3D tracking accuracy, 2D tracking, and novel view synthesis. Experimental results highlight significant improvements compared to baselines, with substantial gains in 3D end-point error (EPE) and improved metrics for tracking accuracy.
Quantitative Evaluation
- 3D Tracking: The method achieves lower EPE and higher percentages within strict thresholds compared to prior works.
- 2D Tracking: It demonstrates superior performance in average Jaccard, average position accuracy, and occlusion accuracy.
- Novel View Synthesis: Achieves higher PSNR and SSIM, and lower LPIPS, setting new benchmarks in visual quality for dynamic scenes.
Practical and Theoretical Implications
Practically, this method can be instrumental in various applications ranging from augmented reality to autonomous navigation where understanding dynamic environments from monocular videos is pivotal. Theoretically, the adoption of motion bases and the integration of multiple prior forms advance the state of research in dynamic scene understanding. This framework paves the way for future explorations into more generalized and real-time solutions.
Future Directions
Future research can aim to address the limitations of per-scene optimization, potentially integrating generative models or deep neural networks for real-time applications. Additionally, extending this framework to handle extreme viewpoint variations and integrating more robust motion priors can further enhance the method's applicability.
In summary, the paper presents a noteworthy advancement in the field of monocular dynamics, offering robust solutions for long-range 3D tracking and high-quality novel view synthesis, and setting a new precedent for future research endeavors in 4D reconstruction.
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