Catalyst4D: High-Fidelity 3D-to-4D Scene Editing via Dynamic Propagation
- URL: http://arxiv.org/abs/2603.12766v1
- Date: Fri, 13 Mar 2026 08:10:27 GMT
- Title: Catalyst4D: High-Fidelity 3D-to-4D Scene Editing via Dynamic Propagation
- Abstract summary: We introduce Catalyst4D, a framework that transfers high-quality 3D edits to dynamic 4D Gaussian scenes.<n> AMG builds a set of structurally stable and spatially representative anchors from both original and edited Gaussians.<n>CUAR preserves temporal appearance consistency by estimating per-Gaussian color uncertainty.
- Score: 51.4527986348543
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advances in 3D scene editing using NeRF and 3DGS enable high-quality static scene editing. In contrast, dynamic scene editing remains challenging, as methods that directly extend 2D diffusion models to 4D often produce motion artifacts, temporal flickering, and inconsistent style propagation. We introduce Catalyst4D, a framework that transfers high-quality 3D edits to dynamic 4D Gaussian scenes while maintaining spatial and temporal coherence. At its core, Anchor-based Motion Guidance (AMG) builds a set of structurally stable and spatially representative anchors from both original and edited Gaussians. These anchors serve as robust region-level references, and their correspondences are established via optimal transport to enable consistent deformation propagation without cross-region interference or motion drift. Complementarily, Color Uncertainty-guided Appearance Refinement (CUAR) preserves temporal appearance consistency by estimating per-Gaussian color uncertainty and selectively refining regions prone to occlusion-induced artifacts. Extensive experiments demonstrate that Catalyst4D achieves temporally stable, high-fidelity dynamic scene editing and outperforms existing methods in both visual quality and motion coherence.
Related papers
- Follow Your Track: Precise Skeleton Animation Controlled by 3D Trajectories [93.39170866007431]
4D generation aims to animate 3D objects with realistic motion, holding great promise for applications.<n>Existing methods typically decouple 3D asset generation from motion synthesis.<n>We present textttACT, a trajectory-conditioned framework for topology-general skeletal animation.
arXiv Detail & Related papers (2026-06-24T03:18:10Z) - MotionScale: Reconstructing Appearance, Geometry, and Motion of Dynamic Scenes with Scalable 4D Gaussian Splatting [54.859943475818234]
MotionScale is a 4D Gaussian Splatting framework that scales efficiently to large scenes and extended sequences.<n>Experiments on challenging real-world benchmarks demonstrate that MotionScale significantly outperforms state-of-the-art methods in both reconstruction quality and temporal stability.
arXiv Detail & Related papers (2026-03-31T06:03:59Z) - Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting [17.957935001501106]
We present Mango-GS, a node-guided framework for high-fidelity 4D reconstruction.<n>We leverage a temporal Transformer to model motion dependencies within a short window of frames.<n>Mango-GS achieves state-of-the-art reconstruction quality and real-time rendering speed.
arXiv Detail & Related papers (2026-03-12T05:00:16Z) - RU4D-SLAM: Reweighting Uncertainty in Gaussian Splatting SLAM for 4D Scene Reconstruction [8.13353479857245]
4D reconstruction, especially 4D Gaussian splatting, offers a promising direction for addressing these challenges.<n>We propose a robust and efficient framework, namely Reweighting Uncertainty in Gaussian Splatting SLAM (RU4D-SLAM) for 4D scene reconstruction.<n>Our method substantially outperforms state-of-the-art approaches in both trajectory accuracy and 4D scene reconstruction.
arXiv Detail & Related papers (2026-02-24T11:47:43Z) - MoRel: Long-Range Flicker-Free 4D Motion Modeling via Anchor Relay-based Bidirectional Blending with Hierarchical Densification [10.799902862870288]
MoRel is a novel framework for temporally consistent and memory-efficient modeling of long-range dynamic scenes.<n>Our approach mitigates temporal discontinuities and flickering artifacts.<n>It achieves temporally coherent and flicker-free long-range 4D reconstruction while maintaining bounded memory usage.
arXiv Detail & Related papers (2025-12-10T02:49:09Z) - Motion4D: Learning 3D-Consistent Motion and Semantics for 4D Scene Understanding [54.859943475818234]
We present Motion4D, a novel framework that integrates 2D priors from foundation models into a unified 4D Gaussian Splatting representation.<n>Our method features a two-part iterative optimization framework: 1) Sequential optimization, which updates motion and semantic fields in consecutive stages to maintain local consistency, and 2) Global optimization, which jointly refines all attributes for long-term coherence.<n>Our method significantly outperforms both 2D foundation models and existing 3D-based approaches across diverse scene understanding tasks, including point-based tracking, video object segmentation, and novel view synthesis.
arXiv Detail & Related papers (2025-12-03T09:32:56Z) - 4D3R: Motion-Aware Neural Reconstruction and Rendering of Dynamic Scenes from Monocular Videos [52.89084603734664]
We present 4D3R, a pose-free dynamic neural rendering framework that decouples static and dynamic components through a two-stage approach.<n>Our approach achieves up to 1.8dB PSNR improvement over state-of-the-art methods.
arXiv Detail & Related papers (2025-11-07T13:25:50Z) - ShapeGen4D: Towards High Quality 4D Shape Generation from Videos [85.45517487721257]
We introduce a native video-to-4D shape generation framework that synthesizes a single dynamic 3D representation end-to-end from the video.<n>Our method accurately captures non-rigid motion, volume changes, and even topological transitions without per-frame optimization.
arXiv Detail & Related papers (2025-10-07T17:58:11Z) - UnIRe: Unsupervised Instance Decomposition for Dynamic Urban Scene Reconstruction [36.00679909382783]
We propose UnIRe, a 3D Splatting (3DGS) based approach that decomposes a scene into a static background and individual dynamic instances.<n>At its core, we introduce 4D superpoints, a novel representation that clusters multi-frame LiDAR points in 4D space.<n>Experiments show that our method outperforms existing methods in dynamic scene reconstruction while enabling accurate and flexible instance-level editing.
arXiv Detail & Related papers (2025-04-01T13:15:58Z) - 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)
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.