MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
- URL: http://arxiv.org/abs/2405.17421v2
- Date: Fri, 29 Nov 2024 18:53:12 GMT
- Title: MoSca: Dynamic Gaussian Fusion from Casual Videos via 4D Motion Scaffolds
- Authors: Jiahui Lei, Yijia Weng, Adam Harley, Leonidas Guibas, Kostas Daniilidis,
- Abstract summary: 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.
Experiments demonstrate state-of-the-art performance on dynamic rendering benchmarks and its effectiveness on real videos.
- Score: 27.802537831023347
- License:
- Abstract: 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. To address such a challenging and ill-posed inverse problem, we leverage prior knowledge from foundational vision models and lift the video data to a novel Motion Scaffold (MoSca) representation, which compactly and smoothly encodes the underlying motions/deformations. The scene geometry and appearance are then disentangled from the deformation field and are encoded by globally fusing the Gaussians anchored onto the MoSca and optimized via Gaussian Splatting. Additionally, camera focal length and poses can be solved using bundle adjustment without the need of any other pose estimation tools. Experiments demonstrate state-of-the-art performance on dynamic rendering benchmarks and its effectiveness on real videos.
Related papers
- 4D Gaussian Splatting: Modeling Dynamic Scenes with Native 4D Primitives [116.2042238179433]
In this paper, we frame dynamic scenes as unconstrained 4D volume learning problems.
We represent a target dynamic scene using a collection of 4D Gaussian primitives with explicit geometry and appearance features.
This approach can capture relevant information in space and time by fitting the underlying photorealistic-temporal volume.
Notably, our 4DGS model is the first solution that supports real-time rendering of high-resolution, novel views for complex dynamic scenes.
arXiv Detail & Related papers (2024-12-30T05:30:26Z) - Deblur4DGS: 4D Gaussian Splatting from Blurry Monocular Video [64.38566659338751]
We propose the first 4D Gaussian Splatting framework to reconstruct a high-quality 4D model from blurry monocular video, named Deblur4DGS.
We introduce exposure regularization to avoid trivial solutions, as well as multi-frame and multi-resolution consistency ones to alleviate artifacts. Beyond novel-view, Deblur4DGS can be applied to improve blurry video from multiple perspectives, including deblurring, frame synthesis, and video stabilization.
arXiv Detail & Related papers (2024-12-09T12:02:11Z) - 4D Gaussian Splatting in the Wild with Uncertainty-Aware Regularization [43.81271239333774]
We propose a novel 4D Gaussian Splatting (4DGS) algorithm for dynamic scenes from casually recorded monocular videos.
Our experiments show that the proposed method improves the performance of 4DGS reconstruction from a video captured by a handheld monocular camera.
arXiv Detail & Related papers (2024-11-13T18:56:39Z) - 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) - MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos [65.31707882676292]
MoDGS is a new pipeline to render novel views of dynamic scenes from a casually captured monocular video.
Experiments demonstrate MoDGS is able to render high-quality novel view images of dynamic scenes from just a casually captured monocular video.
arXiv Detail & Related papers (2024-06-01T13:20:46Z) - 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) - DRSM: efficient neural 4d decomposition for dynamic reconstruction in
stationary monocular cameras [21.07910546072467]
We present a novel framework to tackle 4D decomposition problem for dynamic scenes in monocular cameras.
Our framework utilizes decomposed static and dynamic feature planes to represent 4D scenes and emphasizes the learning of dynamic regions through dense ray casting.
arXiv Detail & Related papers (2024-02-01T16:38:51Z) - Real-time Photorealistic Dynamic Scene Representation and Rendering with
4D Gaussian Splatting [8.078460597825142]
Reconstructing dynamic 3D scenes from 2D images and generating diverse views over time is challenging due to scene complexity and temporal dynamics.
We propose to approximate the underlying-temporal rendering volume of a dynamic scene by optimizing a collection of 4D primitives, with explicit geometry and appearance modeling.
Our model is conceptually simple, consisting of a 4D Gaussian parameterized by anisotropic ellipses that can rotate arbitrarily in space and time, as well as view-dependent and time-evolved appearance represented by the coefficient of 4D spherindrical harmonics.
arXiv Detail & Related papers (2023-10-16T17:57:43Z) - Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis [58.5779956899918]
We present a method that simultaneously addresses the tasks of dynamic scene novel-view synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements.
We follow an analysis-by-synthesis framework, inspired by recent work that models scenes as a collection of 3D Gaussians.
We demonstrate a large number of downstream applications enabled by our representation, including first-person view synthesis, dynamic compositional scene synthesis, and 4D video editing.
arXiv Detail & Related papers (2023-08-18T17:59:21Z)
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.