MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2410.07707v1
- Date: Thu, 10 Oct 2024 08:19:47 GMT
- Title: MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
- Authors: Ruijie Zhu, Yanzhe Liang, Hanzhi Chang, Jiacheng Deng, Jiahao Lu, Wenfei Yang, Tianzhu Zhang, Yongdong Zhang,
- Abstract summary: 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.
- Score: 56.785233997533794
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Dynamic scene reconstruction is a long-term challenge in the field of 3D vision. Recently, the emergence of 3D Gaussian Splatting has provided new insights into this problem. Although subsequent efforts rapidly extend static 3D Gaussian to dynamic scenes, they often lack explicit constraints on object motion, leading to optimization difficulties and performance degradation. To address the above issues, we propose a novel deformable 3D Gaussian splatting framework called MotionGS, which explores explicit motion priors to guide the deformation of 3D Gaussians. Specifically, we first introduce an optical flow decoupling module that decouples optical flow into camera flow and motion flow, corresponding to camera movement and object motion respectively. Then the motion flow can effectively constrain the deformation of 3D Gaussians, thus simulating the motion of dynamic objects. Additionally, a camera pose refinement module is proposed to alternately optimize 3D Gaussians and camera poses, mitigating the impact of inaccurate camera poses. Extensive experiments in the monocular dynamic scenes validate that MotionGS surpasses state-of-the-art methods and exhibits significant superiority in both qualitative and quantitative results. Project page: https://ruijiezhu94.github.io/MotionGS_page
Related papers
- 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) - CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion Blur Images [12.603775893040972]
We propose continuous rigid motion-aware gaussian splatting (CRiM-GS) to reconstruct accurate 3D scene from blurry images with real-time rendering speed.
We leverage rigid body transformations to model the camera motion with proper regularization, preserving the shape and size of the object.
Furthermore, we introduce a continuous deformable 3D transformation in the textitSE(3) field to adapt the rigid body transformation to real-world problems.
arXiv Detail & Related papers (2024-07-04T13:37:04Z) - DreamPhysics: Learning Physical Properties of Dynamic 3D Gaussians with Video Diffusion Priors [75.83647027123119]
We propose to learn the physical properties of a material field with video diffusion priors.
We then utilize a physics-based Material-Point-Method simulator to generate 4D content with realistic motions.
arXiv Detail & Related papers (2024-06-03T16:05:25Z) - MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds [27.802537831023347]
We introduce 4D Motion Scaffolds (MoSca), a neural information processing system designed to reconstruct and synthesize novel views of dynamic scenes from monocular videos captured casually in the wild.
Experiments demonstrate state-of-the-art performance on dynamic rendering benchmarks.
arXiv Detail & Related papers (2024-05-27T17:59:07Z) - DeblurGS: Gaussian Splatting for Camera Motion Blur [45.13521168573883]
We propose DeblurGS, a method to optimize sharp 3D Gaussian Splatting from motion-blurred images.
We restore a fine-grained sharp scene by leveraging the remarkable reconstruction capability of 3D Gaussian Splatting.
Our approach estimates the 6-Degree-of-Freedom camera motion for each blurry observation and synthesizes corresponding blurry renderings.
arXiv Detail & Related papers (2024-04-17T13:14:52Z) - SC4D: Sparse-Controlled Video-to-4D Generation and Motion Transfer [57.506654943449796]
We propose an efficient, sparse-controlled video-to-4D framework named SC4D that decouples motion and appearance.
Our method surpasses existing methods in both quality and efficiency.
We devise a novel application that seamlessly transfers motion onto a diverse array of 4D entities.
arXiv Detail & Related papers (2024-04-04T18:05:18Z) - Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction [89.53963284958037]
We propose a novel motion-aware enhancement framework for dynamic scene reconstruction.
Specifically, we first establish a correspondence between 3D Gaussian movements and pixel-level flow.
For the prevalent deformation-based paradigm that presents a harder optimization problem, a transient-aware deformation auxiliary module is proposed.
arXiv Detail & Related papers (2024-03-18T03:46:26Z) - Motion Guided 3D Pose Estimation from Videos [81.14443206968444]
We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose.
In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced.
We design a new graph convolutional network architecture, U-shaped GCN (UGCN), which captures both short-term and long-term motion information.
arXiv Detail & Related papers (2020-04-29T06:59:30Z)
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.