LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field

• URL: http://arxiv.org/abs/2404.08966v2
• Date: Tue, 16 Apr 2024 14:16:40 GMT
• Title: LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field
• Authors: Jiyang Li, Lechao Cheng, Zhangye Wang, Tingting Mu, Jingxuan He,
• Abstract summary: Cinemagraph is a form of visual media that combines elements of still photography and subtle motion to create a captivating experience. We propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling. Experiment results validate the effectiveness of our approach, demonstrating high-quality and visually appealing scene generation.
• Score: 13.815932949774858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cinemagraph is a unique form of visual media that combines elements of still photography and subtle motion to create a captivating experience. However, the majority of videos generated by recent works lack depth information and are confined to the constraints of 2D image space. In this paper, inspired by significant progress in the field of novel view synthesis (NVS) achieved by 3D Gaussian Splatting (3D-GS), we propose LoopGaussian to elevate cinemagraph from 2D image space to 3D space using 3D Gaussian modeling. To achieve this, we first employ the 3D-GS method to reconstruct 3D Gaussian point clouds from multi-view images of static scenes,incorporating shape regularization terms to prevent blurring or artifacts caused by object deformation. We then adopt an autoencoder tailored for 3D Gaussian to project it into feature space. To maintain the local continuity of the scene, we devise SuperGaussian for clustering based on the acquired features. By calculating the similarity between clusters and employing a two-stage estimation method, we derive an Eulerian motion field to describe velocities across the entire scene. The 3D Gaussian points then move within the estimated Eulerian motion field. Through bidirectional animation techniques, we ultimately generate a 3D Cinemagraph that exhibits natural and seamlessly loopable dynamics. Experiment results validate the effectiveness of our approach, demonstrating high-quality and visually appealing scene generation. The project is available at https://pokerlishao.github.io/LoopGaussian/.

Related papers

• DreamScene360: Unconstrained Text-to-3D Scene Generation with Panoramic Gaussian Splatting [56.101576795566324]
  We present a text-to-3D 360$circ$ scene generation pipeline. Our approach utilizes the generative power of a 2D diffusion model and prompt self-refinement. Our method offers a globally consistent 3D scene within a 360$circ$ perspective.
  arXiv  Detail & Related papers  (2024-04-10T10:46:59Z)
• Bridging 3D Gaussian and Mesh for Freeview Video Rendering [57.21847030980905]
  GauMesh bridges the 3D Gaussian and Mesh for modeling and rendering the dynamic scenes. We show that our approach adapts the appropriate type of primitives to represent the different parts of the dynamic scene.
  arXiv  Detail & Related papers  (2024-03-18T04:01:26Z)
• Rig3DGS: Creating Controllable Portraits from Casual Monocular Videos [33.779636707618785]
  We introduce Rig3DGS to create controllable 3D human portraits from casual smartphone videos. Key innovation is a carefully designed deformation method which is guided by a learnable prior derived from a 3D morphable model. We demonstrate the effectiveness of our learned deformation through extensive quantitative and qualitative experiments.
  arXiv  Detail & Related papers  (2024-02-06T05:40:53Z)
• BerfScene: Bev-conditioned Equivariant Radiance Fields for Infinite 3D Scene Generation [96.58789785954409]
  We propose a practical and efficient 3D representation that incorporates an equivariant radiance field with the guidance of a bird's-eye view map. We produce large-scale, even infinite-scale, 3D scenes via synthesizing local scenes and then stitching them with smooth consistency.
  arXiv  Detail & Related papers  (2023-12-04T18:56:10Z)
• Gaussian Grouping: Segment and Edit Anything in 3D Scenes [65.49196142146292]
  We propose Gaussian Grouping, which extends Gaussian Splatting to jointly reconstruct and segment anything in open-world 3D scenes. Compared to the implicit NeRF representation, we show that the grouped 3D Gaussians can reconstruct, segment and edit anything in 3D with high visual quality, fine granularity and efficiency.
  arXiv  Detail & Related papers  (2023-12-01T17:09:31Z)
• LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes [52.31402192831474]
  Existing 3D scene generation models, however, limit the target scene to specific domain. We propose LucidDreamer, a domain-free scene generation pipeline. LucidDreamer produces highly-detailed Gaussian splats with no constraint on domain of the target scene.
  arXiv  Detail & Related papers  (2023-11-22T13:27:34Z)
• 3D Cinemagraphy from a Single Image [73.09720823592092]
  We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion.
  arXiv  Detail & Related papers  (2023-03-10T06:08:23Z)

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.