Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis

• URL: http://arxiv.org/abs/2308.09713v1
• Date: Fri, 18 Aug 2023 17:59:21 GMT
• Title: Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis
• Authors: Jonathon Luiten and Georgios Kopanas and Bastian Leibe and Deva Ramanan
• Abstract summary: 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.
• Score: 58.5779956899918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 which are optimized to reconstruct input images via differentiable rendering. To model dynamic scenes, we allow Gaussians to move and rotate over time while enforcing that they have persistent color, opacity, and size. By regularizing Gaussians' motion and rotation with local-rigidity constraints, we show that our Dynamic 3D Gaussians correctly model the same area of physical space over time, including the rotation of that space. Dense 6-DOF tracking and dynamic reconstruction emerges naturally from persistent dynamic view synthesis, without requiring any correspondence or flow as input. 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.

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)
• 4D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes [33.14021987166436]
  We introduce 4DRotorGS, a novel method that represents dynamic scenes with anisotropic 4D XYZT Gaussians. As an explicit spatial-temporal representation, 4DRotorGS demonstrates powerful capabilities for modeling complicated dynamics and fine details. We further implement our temporal slicing and acceleration framework, achieving real-time rendering speeds of up to 277 FPS on an 3090 GPU and 583 FPS on a 4090 GPU.
  arXiv  Detail & Related papers  (2024-02-05T18:59:04Z)
• Align Your Gaussians: Text-to-4D with Dynamic 3D Gaussians and Composed Diffusion Models [94.07744207257653]
  We focus on the underexplored text-to-4D setting and synthesize dynamic, animated 3D objects. We combine text-to-image, text-to-video, and 3D-aware multiview diffusion models to provide feedback during 4D object optimization.
  arXiv  Detail & Related papers  (2023-12-21T11:41:02Z)
• SWinGS: Sliding Windows for Dynamic 3D Gaussian Splatting [7.553079256251747]
  We extend 3D Gaussian Splatting to reconstruct dynamic scenes. We produce high-quality renderings of general dynamic scenes with competitive quantitative performance. Our method can be viewed in real-time in our dynamic interactive viewer.
  arXiv  Detail & Related papers  (2023-12-20T03:54:03Z)
• DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes [57.12439406121721]
  We present DrivingGaussian, an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects, we first sequentially and progressively model the static background of the entire scene. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency.
  arXiv  Detail & Related papers  (2023-12-13T06:30:51Z)
• SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes [59.23385953161328]
  Novel view synthesis for dynamic scenes is still a challenging problem in computer vision and graphics. We propose a new representation that explicitly decomposes the motion and appearance of dynamic scenes into sparse control points and dense Gaussians. Our method can enable user-controlled motion editing while retaining high-fidelity appearances.
  arXiv  Detail & Related papers  (2023-12-04T11:57:14Z)
• DynMF: Neural Motion Factorization for Real-time Dynamic View Synthesis with 3D Gaussian Splatting [35.69069478773709]
  We argue that the per-point motions of a dynamic scene can be decomposed into a small set of explicit or learned trajectories. Our representation is interpretable, efficient, and expressive enough to offer real-time view synthesis of complex dynamic scene motions.
  arXiv  Detail & Related papers  (2023-11-30T18:59:11Z)
• 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)
• Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes [70.76742458931935]
  We introduce a new representation that models the dynamic scene as a time-variant continuous function of appearance, geometry, and 3D scene motion. Our representation is optimized through a neural network to fit the observed input views. We show that our representation can be used for complex dynamic scenes, including thin structures, view-dependent effects, and natural degrees of motion.
  arXiv  Detail & Related papers  (2020-11-26T01:23:44Z)

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.