Kinematics-Driven Gaussian Shape Deformation for Blurry Monocular Dynamic Scenes

• URL: http://arxiv.org/abs/2605.08635v1
• Date: Sat, 09 May 2026 03:01:17 GMT
• Title: Kinematics-Driven Gaussian Shape Deformation for Blurry Monocular Dynamic Scenes
• Abstract summary: We present Kinematics-GS, a kinematics-aware framework that models blur as motion-aligned deformation.<n>To stabilize optimization, we decompose scenes into dynamic and static components using temporal deformation variance.<n>We also introduce a challenging real-world dataset of deformable and elastic objects exhibiting non-rigid motion.
• Score: 32.40228197424996
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reconstructing dynamic 3D scenes from blurry monocular videos is challenging as motion-induced blur entangles object motion and geometry, hindering geometric consistency. We present Kinematics-GS, a kinematics-aware framework that models blur as motion-aligned deformation and introduces a kinematic prior to reparameterize Gaussian shapes along motion trajectories, thereby mitigating degenerate shape collapse without auxiliary motion supervision. To stabilize optimization, we decompose scenes into dynamic and static components using temporal deformation variance and employ a coarse-to-fine deformation strategy to capture both global motion and fine-grained details. We also introduce a challenging real-world dataset of deformable and elastic objects exhibiting non-rigid motion with spatially non-uniform motion blur that obscures geometric cues. Extensive experiments on real-world benchmarks with realistic motion blur demonstrate that Kinematics-GS outperforms prior methods by a clear margin in monocular dynamic scene reconstruction, highlighting its effectiveness in handling complex and non-rigid motion scenarios.

Related papers

• BLARM: Animating 3D Objects from Video via Blending Latent Rigid Motion Primitives [14.13122283409208]
  BLARM predicts a temporally coherent animated mesh whose motion follows the video.<n>We represent animation using a compact set of learned, time-varying rigid motion components.<n> BLARM produces accurate and temporally stable animations while recovering compact, interpretable motion structure from monocular video.
  arXiv  Detail & Related papers  (2026-08-31T17:21:54Z)
• MotionGS-SLAM: Event-Modulated Gaussian Splatting for Motion-Blur Robust SLAM [10.233658861023612]
  Current Vision-based SLAM systems fail catastrophically when motion blur corrupts the visual input.<n>We present MotionGS-SLAM, which reimagines motion blur handling through a paradigm shift.<n>We show significant improvements over state-of-the-art methods in trajectory accuracy and map quality under severe high-motion conditions.
  arXiv  Detail & Related papers  (2026-08-15T04:20:51Z)
• MotionScale: Reconstructing Appearance, Geometry, and Motion of Dynamic Scenes with Scalable 4D Gaussian Splatting [54.859943475818234]
  MotionScale is a 4D Gaussian Splatting framework that scales efficiently to large scenes and extended sequences.<n>Experiments on challenging real-world benchmarks demonstrate that MotionScale significantly outperforms state-of-the-art methods in both reconstruction quality and temporal stability.
  arXiv  Detail & Related papers  (2026-03-31T06:03:59Z)
• MOSAIC-GS: Monocular Scene Reconstruction via Advanced Initialization for Complex Dynamic Environments [12.796165448365949]
  MOSAIC-GS is a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos.<n>We leverage multiple geometric cues, such as depth, optical flow, dynamic object segmentation, and point tracking.<n>We demonstrate that MOSAIC-GS achieves substantially faster optimization and rendering compared to existing methods.
  arXiv  Detail & Related papers  (2026-01-08T20:48:24Z)
• 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos [52.89084603734664]
  We present 4D3R, a pose-free dynamic neural rendering framework that decouples static and dynamic components through a two-stage approach.<n>Our approach achieves up to 1.8dB PSNR improvement over state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-07T13:25:50Z)
• DynaSplat: Dynamic-Static Gaussian Splatting with Hierarchical Motion Decomposition for Scene Reconstruction [9.391616497099422]
  We present DynaSplat, an approach that extends Gaussian Splatting to dynamic scenes.<n>We classify scene elements as static or dynamic through a novel fusion of deformation offset statistics and 2D motion flow consistency.<n>We then introduce a hierarchical motion modeling strategy that captures both coarse global transformations and fine-grained local movements.
  arXiv  Detail & Related papers  (2025-06-11T15:13:35Z)
• Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction [86.099855111676]
  Traditional SLAM systems struggle with highly dynamic scenes commonly found in casual videos.<n>This work leverages a 3D point tracker to separate the camera-induced motion from the observed motion of dynamic objects.<n>Our framework combines the core of traditional SLAM -- bundle adjustment -- with a robust learning-based 3D tracker front-end.
  arXiv  Detail & Related papers  (2025-04-20T07:29:42Z)
• CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images [19.08403715388913]
  3D Gaussian Splatting has gained significant attention due to its high-quality novel view rendering.<n>A critical issue is the camera motion blur caused by movement during exposure, which hinders accurate 3D scene reconstruction.<n>We propose CoMoGaussian, a Continuous Motion-Aware Gaussian Splatting that reconstructs precise 3D scenes from motion-blurred images.
  arXiv  Detail & Related papers  (2025-03-07T11:18:43Z)
• Event-boosted Deformable 3D Gaussians for Dynamic Scene Reconstruction [50.873820265165975]
  We introduce the first approach combining event cameras, which capture high-temporal-resolution, continuous motion data, with deformable 3D-GS for dynamic scene reconstruction.<n>We propose a GS-Threshold Joint Modeling strategy, creating a mutually reinforcing process that greatly improves both 3D reconstruction and threshold modeling.<n>We contribute the first event-inclusive 4D benchmark with synthetic and real-world dynamic scenes, on which our method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-25T08:23:38Z)
• MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting [56.785233997533794]
  We propose a novel deformable 3D Gaussian splatting framework called MotionGS. MotionGS explores explicit motion priors to guide the deformation of 3D Gaussians. Experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-10T08:19:47Z)
• Shape of Motion: 4D Reconstruction from a Single Video [42.42669078777769]
  We introduce a method for reconstructing generic dynamic scenes, featuring explicit, persistent 3D motion trajectories in the world coordinate frame.<n>First, we exploit the low-dimensional structure of 3D motion by representing scene motion with a compact set of SE(3) motion bases.<n>Second, we take advantage of off-the-shelf data-driven priors such as monocular depth maps and long-range 2D tracks, and devise a method to effectively consolidate these noisy supervisory signals.
  arXiv  Detail & Related papers  (2024-07-18T17:59:08Z)
• MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds [27.802537831023347]
  We introduce 4D Motion Scaffolds (MoSca), a modern 4D reconstruction system designed to reconstruct and synthesize novel views of dynamic scenes from monocular videos captured casually in the wild.<n> Experiments demonstrate state-of-the-art performance on dynamic rendering benchmarks and its effectiveness on real videos.
  arXiv  Detail & Related papers  (2024-05-27T17:59:07Z)
• SceNeRFlow: Time-Consistent Reconstruction of General Dynamic Scenes [75.9110646062442]
  We propose SceNeRFlow to reconstruct a general, non-rigid scene in a time-consistent manner. Our method takes multi-view RGB videos and background images from static cameras with known camera parameters as input. We show experimentally that, unlike prior work that only handles small motion, our method enables the reconstruction of studio-scale motions.
  arXiv  Detail & Related papers  (2023-08-16T09:50:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.