title: SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D
    Gaussians from Monocular Video
summary: Synthesizing novel views from in-the-wild monocular videos is challenging due
  to scene dynamics and the lack of multi-view cues. To address this, we propose
  SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for
  high-quality reconstruction and fast rendering from monocular videos. At its
  core is a novel Motion-Adaptive Spline (MAS) method, which represents
  continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a
  small number of control points. For MAS, we introduce a Motion-Adaptive Control
  points Pruning (MACP) method to model the deformation of each dynamic 3D
  Gaussian across varying motions, progressively pruning control points while
  maintaining dynamic modeling integrity. Additionally, we present a joint
  optimization strategy for camera parameter estimation and 3D Gaussian
  attributes, leveraging photometric and geometric consistency. This eliminates
  the need for Structure-from-Motion preprocessing and enhances SplineGS's
  robustness in real-world conditions. Experiments show that SplineGS
  significantly outperforms state-of-the-art methods in novel view synthesis
  quality for dynamic scenes from monocular videos, achieving thousands times
  faster rendering speed.
paper_authors._id: 67622196ebfe62ccbf72e469
paper_authors.name: Jongmin Park
paper_authors._id: 67622196ebfe62ccbf72e46a
paper_authors.name: Minh-Quan Viet Bui
paper_authors._id: 67622196ebfe62ccbf72e46b
paper_authors.name: Juan Luis Gonzalez Bello
paper_authors._id: 67622196ebfe62ccbf72e46c
paper_authors.name: Jaeho Moon
paper_authors._id: 67622196ebfe62ccbf72e46d
paper_authors.name: Jihyong Oh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-18T12:38:34.804Z
paper_authors._id: 6576b99d58ce19fa1e33eb1d
paper_authors.avatarUrl: /avatars/b533e776aa3d95d722b46ef0cd381acd.svg
paper_authors.fullname: Jihyong Oh
paper_authors.type: user
paper_authors.user: ozbro
paper_authors._id: 67622196ebfe62ccbf72e46e
paper_authors.name: Munchurl Kim
paper_title: SplineGS: Robust Motion-Adaptive Spline for Real-Time Dynamic 3D
    Gaussians from Monocular Video
paper_summary: Synthesizing novel views from in-the-wild monocular videos is challenging due
  to scene dynamics and the lack of multi-view cues. To address this, we propose
  SplineGS, a COLMAP-free dynamic 3D Gaussian Splatting (3DGS) framework for
  high-quality reconstruction and fast rendering from monocular videos. At its
  core is a novel Motion-Adaptive Spline (MAS) method, which represents
  continuous dynamic 3D Gaussian trajectories using cubic Hermite splines with a
  small number of control points. For MAS, we introduce a Motion-Adaptive Control
  points Pruning (MACP) method to model the deformation of each dynamic 3D
  Gaussian across varying motions, progressively pruning control points while
  maintaining dynamic modeling integrity. Additionally, we present a joint
  optimization strategy for camera parameter estimation and 3D Gaussian
  attributes, leveraging photometric and geometric consistency. This eliminates
  the need for Structure-from-Motion preprocessing and enhances SplineGS's
  robustness in real-world conditions. Experiments show that SplineGS
  significantly outperforms state-of-the-art methods in novel view synthesis
  quality for dynamic scenes from monocular videos, achieving thousands times
  faster rendering speed.
paper_ai_keywords: COLMAP-free dynamic 3D Gaussian Splatting Motion-Adaptive Spline cubic Hermite splines Motion-Adaptive Control points Pruning joint optimization strategy camera parameter estimation 3D Gaussian attributes photometric and geometric consistency novel view synthesis dynamic scenes monocular videos