WebSpline: Structure-Informed Splines for Real-Time 3D Gaussians from Monocular Videos
WebSpline employs structure-guided splines for real-time, high-fidelity 3D Gaussian reconstruction from monocular videos, outperforming prior methods in quality and speed.
Key Findings
Methodology
WebSpline integrates a Structural Proxy Graph (SPG) with a Structure-Informed Spline (SIS), optimized in two stages. First, SPG is initialized from 2D point tracks and refined with temporal rigidity regularization to ensure structural coherence. Then, guided by the refined SPG, the SIS models each dynamic Gaussian's trajectory using learnable cubic Hermite splines, constrained by spatial and structural neighborhoods. During inference, only the learned SIS is evaluated, enabling fast rendering. The core algorithms include cubic Hermite spline interpolation and neighborhood regularizations, leveraging depth and motion cues.
Key Results
- On iPhone and NVIDIA benchmarks, WebSpline surpasses state-of-the-art methods with PSNR improvements of +0.36dB and +0.61dB respectively, and achieves over 10× faster rendering (278 FPS vs. 27 FPS). It demonstrates superior detail preservation and structural consistency in complex scenes.
- On the iPhone dataset, WebSpline outperforms competitors in perceptual metrics (LPIPS reduction of 0.05) and training efficiency, validating its robustness.
- Ablation studies confirm that the SPG and two-stage optimization are crucial; removing them results in noisier, less coherent motion and degraded quality.
Significance
This approach advances monocular dynamic scene reconstruction by balancing structural stability, detail fidelity, and rendering speed. It addresses longstanding challenges in real-time, high-quality 3D scene modeling, enabling applications in VR, AR, film, and robotics. By introducing structure-guided splines, WebSpline offers a scalable, robust solution that bridges the gap between detailed modeling and real-time performance, paving the way for more immersive virtual experiences and intelligent scene understanding.
Technical Contribution
WebSpline's main innovation lies in combining a structural proxy graph with a learnable spline-based motion model, enabling stable, detailed, and fast dynamic scene reconstruction from monocular videos. The two-stage optimization ensures structural coherence while capturing fine-grained motion. Its inference-only evaluation of the SIS allows for high-speed rendering, significantly outperforming previous blending or purely deep learning-based methods. This framework opens new avenues for explicit, structure-aware dynamic modeling.
Novelty
This work is the first to integrate a structural proxy graph with a learnable spline representation for monocular dynamic scene reconstruction, achieving a unique combination of structural stability, detail fidelity, and real-time rendering. Unlike prior methods relying on motion blending or unconstrained parameterization, WebSpline explicitly models the scene's structure and motion trajectories, providing more reliable and interpretable results.
Limitations
- The model's accuracy diminishes in scenes with severe non-rigid deformations or occlusions, due to reliance on 2D point tracks and structure regularization.
- Training involves multiple stages with considerable computational cost, limiting real-time training scenarios.
- Generalization to large-scale or highly complex scenes remains challenging, requiring further adaptation and multi-modal data integration.
Future Work
Future directions include integrating multi-modal cues like depth and optical flow to enhance robustness, reducing training complexity for broader applicability, and extending to multi-view setups for larger scenes. Additionally, exploring unsupervised or weakly supervised training strategies could improve scalability and real-world deployment.
AI Executive Summary
WebSpline introduces a novel framework for real-time, high-fidelity 3D scene reconstruction from monocular videos, addressing the core challenge of balancing structural coherence with detailed motion modeling. Traditional methods often struggle with either slow rendering speeds or unstable, blurry reconstructions, especially in dynamic scenes with complex non-rigid deformations. WebSpline overcomes these limitations by leveraging a structured approach that combines a Structural Proxy Graph (SPG) with a learnable spline-based motion model (SIS). During training, the SPG is initialized from 2D point tracks and refined with a temporal rigidity loss, ensuring a stable structural foundation. Guided by this, the SIS models each Gaussian's trajectory using cubic Hermite splines, constrained by spatial and structural neighborhoods to preserve local detail and global coherence. The key innovation is that during inference, only the SIS evaluation is needed, enabling rapid rendering at over 278 FPS, far surpassing prior methods like WorldTree. Extensive experiments on iPhone and NVIDIA datasets demonstrate superior quantitative metrics—PSNR improvements of +0.36dB and +0.61dB—and qualitative results showing sharper, more coherent dynamic scene reconstructions. This work significantly advances the state of the art in monocular dynamic scene modeling, with broad implications for immersive VR, film production, and autonomous systems. Future work aims to incorporate multi-modal cues, improve scalability, and extend to larger, more complex scenes, pushing the boundaries of real-time 3D scene understanding.
Deep Dive
Abstract
Dynamic scene reconstruction from monocular videos remains highly challenging, as existing methods often struggle to balance global structural coherence and local fine-grained details under limited multi-view cues. To address this challenge, we propose WebSpline, a novel dynamic 3D Gaussian framework that enables structurally coherent and high-fidelity reconstruction from monocular videos with fast rendering. The core of WebSpline is the Structure-Informed Spline (SIS) representation, which models each dynamic Gaussian trajectory using a learnable cubic Hermite spline whose motion is structurally organized with an auxiliary Structural Proxy Graph (SPG). The proposed framework is optimized in two stages: (i) in the first stage, the SPG is initialized from 2D point tracks and refined with temporal rigidity regularization to establish structural coherence for moving objects across the sequence; and (ii) in the second stage, the SIS representation is initialized from the refined SPG and optimized under both spatial and structural neighborhood constraints. At inference, Gaussian motion is obtained solely by evaluating the learned SIS, enabling fast rendering. Extensive experiments on the challenging monocular dynamic scene benchmarks, iPhone and NVIDIA, demonstrate that our WebSpline achieves state-of-the-art rendering quality while rendering over 10 times faster than WorldTree, the second-best method on the iPhone dataset.