Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video

• URL: http://arxiv.org/abs/2508.06715v1
• Date: Fri, 08 Aug 2025 21:31:51 GMT
• Title: Restage4D: Reanimating Deformable 3D Reconstruction from a Single Video
• Authors: Jixuan He, Chieh Hubert Lin, Lu Qi, Ming-Hsuan Yang,
• Abstract summary: We introduce textbfRestage4D, a geometry-preserving pipeline for video-conditioned 4D restaging.<n>We validate Restage4D on DAVIS and PointOdyssey, demonstrating improved geometry consistency, motion quality, and 3D tracking performance.
• Score: 56.781766315691854
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• ArtHOI: Articulated Human-Object Interaction Synthesis by 4D Reconstruction from Video Priors [51.06020148149403]
  We introduce ArtHOI, the first zero-shot framework for articulated human-object interaction synthesis via 4D reconstruction from video priors.<n>ArtHOI bridges video-based generation and geometry-aware reconstruction, producing interactions that are both semantically aligned and physically grounded.
  arXiv  Detail & Related papers  (2026-03-04T17:58:04Z)
• Motion 3-to-4: 3D Motion Reconstruction for 4D Synthesis [53.48281548500864]
  Motion 3-to-4 is a feed-forward framework for synthesising high-quality 4D dynamic objects from a single monocular video.<n>Our model learns a compact motion latent representation and predicts per-frame trajectories to recover complete robustness, temporally coherent geometry.
  arXiv  Detail & Related papers  (2026-01-20T18:59:48Z)
• Joint 3D Geometry Reconstruction and Motion Generation for 4D Synthesis from a Single Image [88.71287865590273]
  We introduce TrajScene-60K, a large-scale dataset of 60,000 video samples with dense point trajectories.<n>We propose a diffusion-based 4D Scene Trajectory Generator (4D-STraG) to jointly generate geometrically consistent and motion-plausible 4D trajectories.<n>We then propose a 4D View Synthesis Module (4D-Vi) to render videos with arbitrary camera trajectories from 4D point track representations.
  arXiv  Detail & Related papers  (2025-12-04T17:59:10Z)
• Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach [54.559847511280545]
  We present a novel video generation framework that integrates 3-dimensional geometry and dynamic awareness.<n>To achieve this, we augment 2D videos with 3D point trajectories and align them in pixel space.<n>The resulting 3D-aware video dataset, PointVid, is then used to fine-tune a latent diffusion model.
  arXiv  Detail & Related papers  (2025-02-05T21:49:06Z)
• Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models [54.35214051961381]
  3D meshes are widely used in computer vision and graphics for their efficiency in animation and minimal memory use in movies, games, AR, and VR.<n>However, creating temporal consistent and realistic textures for mesh remains labor-intensive for professional artists.<n>We present 3D Tex sequences that integrates inherent geometry from mesh sequences with video diffusion models to produce consistent textures.
  arXiv  Detail & Related papers  (2024-10-14T17:59:59Z)
• TC4D: Trajectory-Conditioned Text-to-4D Generation [94.90700997568158]
  We propose TC4D: trajectory-conditioned text-to-4D generation, which factors motion into global and local components. We learn local deformations that conform to the global trajectory using supervision from a text-to-video model. Our approach enables the synthesis of scenes animated along arbitrary trajectories, compositional scene generation, and significant improvements to the realism and amount of generated motion.
  arXiv  Detail & Related papers  (2024-03-26T17:55:11Z)

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.