Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video
Restage4D leverages single video and motion priors to achieve physically consistent 4D deformable scene reconstruction with geometric correction.
Key Findings
Methodology
Restage4D integrates low-rank decomposed 4D Gaussian Splatting with a video-rewinding joint training scheme. It employs shared motion representations, occlusion-aware ARAP regularization, and disocclusion backtracing to enhance geometric and structural consistency. The approach uses a pre-trained tracking model to initialize motion, applies a geometry-preserving pipeline, and incorporates a disocclusion mechanism to recover missing geometry. The core algorithms involve SE(3) rigid transformations, scene flow regularization, and depth-based occlusion analysis, enabling correction of artifacts from generative models under complex motions.
Key Results
- On DAVIS and PointOdyssey datasets, Restage4D improves geometric consistency (CLIP similarity increased by 3.72 points, volume consistency improved by 0.91), motion quality, and 3D tracking accuracy. Quantitative metrics show PSNR reaching 27.12, and edge length stability improving 3.6 times. Ablation studies confirm the effectiveness of ARAP and backtracing in maintaining geometry during occlusion and complex deformation.
- The method effectively corrects infeasible motions and inconsistent geometries generated by diffusion models, especially under occlusion. It demonstrates superior performance in preserving natural deformation, with robust generalization across diverse scenes and complex motions, outperforming baseline approaches in both qualitative and quantitative evaluations.
Significance
This work advances single-view 4D scene reconstruction by integrating real-world geometric and physical priors, addressing the limitations of existing generative models in complex dynamic scenarios. It enhances the realism and controllability of virtual content, facilitating applications in animation, virtual try-on, and simulation. The approach bridges the gap between data-driven generative models and physics-based reconstruction, offering a new paradigm for physically grounded 4D content creation.
Technical Contribution
The paper introduces a novel geometry-preserving pipeline combining low-rank motion decomposition, video-rewinding joint training, occlusion-aware ARAP regularization, and disocclusion backtracing. These innovations enable robust handling of occlusion, complex deformation, and artifact correction from generative models. The framework maintains geometric fidelity while allowing flexible motion editing, representing a significant step beyond prior monocular deformable 3D reconstruction methods.
Novelty
This is the first work to combine video reverse playback with shared motion representations for single-video 4D scene reanimation. The integration of occlusion-aware rigidity regularization and disocclusion backtracing addresses fundamental challenges in geometry consistency under complex, occluded motions, setting a new standard for realistic deformable scene synthesis from monocular videos.
Limitations
- The model's performance diminishes with extreme occlusion or rapid motion, leading to potential geometric drift. Computational cost remains high, requiring extensive GPU resources. Handling very long sequences or highly dynamic scenes needs further optimization.
Future Work
Future directions include multi-view integration, self-supervised learning to reduce annotation dependence, and physical simulation to enhance motion realism. Extending the framework to real-time applications and broader scene categories will further increase its practical impact.
AI Executive Summary
Creating realistic deformable 4D scenes from a single video remains a significant challenge in computer vision and graphics. Existing methods often struggle with maintaining geometric fidelity and physical plausibility, especially under occlusion and complex motion. This paper introduces Restage4D, a novel pipeline that leverages real-world geometric and physical priors to address these issues.
Restage4D employs a low-rank decomposed 4D Gaussian Splatting representation, combined with a video-rewinding joint training strategy. This approach bridges the temporal gap between the original and synthetic motions, enabling consistent shape and motion sharing. To further improve geometric fidelity, the authors incorporate occlusion-aware ARAP regularization, which enforces local rigidity, and a disocclusion backtracing mechanism that recovers missing geometry in newly visible regions.
Experimental results on DAVIS and PointOdyssey datasets demonstrate that Restage4D significantly outperforms existing methods in geometric consistency, motion quality, and 3D tracking accuracy. Quantitative metrics such as PSNR, CLIP similarity, and edge length stability show marked improvements, confirming the effectiveness of the approach. The method can correct infeasible motions generated by diffusion models, especially under occlusion, and maintain natural deformation behaviors.
This work has profound implications for virtual content creation, enabling more realistic and controllable 4D scene synthesis from monocular videos. It opens new avenues for applications in animation, virtual try-on, and simulation, bridging the gap between generative models and physics-based reconstruction. Future work will explore multi-view integration, real-time processing, and physical simulation to further enhance the robustness and applicability of the framework.
Deep Dive
Abstract
Creating deformable 3D content has gained increasing attention with the rise of text-to-image and image-to-video generative models. While these models provide rich semantic priors for appearance, they struggle to capture the physical realism and motion dynamics needed for authentic 4D scene synthesis. In contrast, real-world videos can provide physically grounded geometry and articulation cues that are difficult to hallucinate. One question is raised: \textit{Can we generate physically consistent 4D content by leveraging the motion priors of the real-world video}? In this work, we explore the task of reanimating deformable 3D scenes from a single video, using the original sequence as a supervisory signal to correct artifacts from synthetic motion. We introduce \textbf{Restage4D}, a geometry-preserving pipeline for video-conditioned 4D restaging. Our approach uses a video-rewinding training strategy to temporally bridge a real base video and a synthetic driving video via a shared motion representation. We further incorporate an occlusion-aware rigidity loss and a disocclusion backtracing mechanism to improve structural and geometry consistency under challenging motion. We validate Restage4D on DAVIS and PointOdyssey, demonstrating improved geometry consistency, motion quality, and 3D tracking performance. Our method not only preserves deformable structure under novel motion, but also automatically corrects errors introduced by generative models, revealing the potential of video prior in 4D restaging task. Source code and trained models will be released.