LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling

• URL: http://arxiv.org/abs/2507.02363v1
• Date: Thu, 03 Jul 2025 06:50:33 GMT
• Title: LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling
• Authors: Jiahao Wu, Rui Peng, Jianbo Jiao, Jiayu Yang, Luyang Tang, Kaiqiang Xiong, Jie Liang, Jinbo Yan, Runling Liu, Ronggang Wang,
• Abstract summary: LocalDyGS is a novel method to model dynamic videos from multi-view inputs for arbitrary viewpoints.<n>Our method is competitive on various fine-scale datasets compared to state-of-the-art (SOTA) methods.
• Score: 33.71658540929536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the complex and highly dynamic motions in the real world, synthesizing dynamic videos from multi-view inputs for arbitrary viewpoints is challenging. Previous works based on neural radiance field or 3D Gaussian splatting are limited to modeling fine-scale motion, greatly restricting their application. In this paper, we introduce LocalDyGS, which consists of two parts to adapt our method to both large-scale and fine-scale motion scenes: 1) We decompose a complex dynamic scene into streamlined local spaces defined by seeds, enabling global modeling by capturing motion within each local space. 2) We decouple static and dynamic features for local space motion modeling. A static feature shared across time steps captures static information, while a dynamic residual field provides time-specific features. These are combined and decoded to generate Temporal Gaussians, modeling motion within each local space. As a result, we propose a novel dynamic scene reconstruction framework to model highly dynamic real-world scenes more realistically. Our method not only demonstrates competitive performance on various fine-scale datasets compared to state-of-the-art (SOTA) methods, but also represents the first attempt to model larger and more complex highly dynamic scenes. Project page: https://wujh2001.github.io/LocalDyGS/.

Related papers

• 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)
• PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model [23.768571323272152]
  PartRM is a novel 4D reconstruction framework that simultaneously models appearance, geometry, and part-level motion from multi-view images of a static object.<n>We introduce the PartDrag-4D dataset, providing multi-view observations of part-level dynamics across over 20,000 states.<n> Experimental results show that PartRM establishes a new state-of-the-art in part-level motion learning and can be applied in manipulation tasks in robotics.
  arXiv  Detail & Related papers  (2025-03-25T17:59:58Z)
• MoDec-GS: Global-to-Local Motion Decomposition and Temporal Interval Adjustment for Compact Dynamic 3D Gaussian Splatting [23.98941743726612]
  MoDecGS is a memory-efficient framework for reconstructing novel views in challenging scenarios with complex motions.<n>MoDecGS achieves an average 70% reduction in model size over stateoftheart methods for dynamic 3D Gaussians from realworld dynamic videos.
  arXiv  Detail & Related papers  (2025-01-07T11:43:13Z)
• UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction [86.4386398262018]
  UrbanGS uses 2D semantic maps and an existing dynamic Gaussian approach to distinguish static objects from the scene.<n>For potentially dynamic objects, we aggregate temporal information using learnable time embeddings.<n>Our approach outperforms state-of-the-art methods in reconstruction quality and efficiency.
  arXiv  Detail & Related papers  (2024-12-04T16:59:49Z)
• 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)
• Shape of Motion: 4D Reconstruction from a Single Video [51.04575075620677]
  We introduce a method capable of reconstructing generic dynamic scenes, featuring explicit, full-sequence-long 3D motion. We exploit the low-dimensional structure of 3D motion by representing scene motion with a compact set of SE3 motion bases. Our method achieves state-of-the-art performance for both long-range 3D/2D motion estimation and novel view synthesis on dynamic scenes.
  arXiv  Detail & Related papers  (2024-07-18T17:59:08Z)
• EgoGaussian: Dynamic Scene Understanding from Egocentric Video with 3D Gaussian Splatting [95.44545809256473]
  EgoGaussian is a method capable of simultaneously reconstructing 3D scenes and dynamically tracking 3D object motion from RGB egocentric input alone. We show significant improvements in terms of both dynamic object and background reconstruction quality compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-06-28T10:39:36Z)
• Dynamic 3D Gaussian Fields for Urban Areas [60.64840836584623]
  We present an efficient neural 3D scene representation for novel-view synthesis (NVS) in large-scale, dynamic urban areas. We propose 4DGF, a neural scene representation that scales to large-scale dynamic urban areas.
  arXiv  Detail & Related papers  (2024-06-05T12:07:39Z)
• DynaVol: Unsupervised Learning for Dynamic Scenes through Object-Centric Voxelization [67.85434518679382]
  We present DynaVol, a 3D scene generative model that unifies geometric structures and object-centric learning. The key idea is to perform object-centric voxelization to capture the 3D nature of the scene. voxel features evolve over time through a canonical-space deformation function, forming the basis for global representation learning.
  arXiv  Detail & Related papers  (2023-04-30T05:29:28Z)
• MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras [98.40768911788854]
  We introduce MoCo-Flow, a representation that models the dynamic scene using a 4D continuous time-variant function. At the heart of our work lies a novel optimization formulation, which is constrained by a motion consensus regularization on the motion flow. We extensively evaluate MoCo-Flow on several datasets that contain human motions of varying complexity.
  arXiv  Detail & Related papers  (2021-06-08T16:03:50Z)

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.