Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting

• URL: http://arxiv.org/abs/2604.21776v2
• Date: Fri, 24 Apr 2026 04:18:46 GMT
• Title: Reshoot-Anything: A Self-Supervised Model for In-the-Wild Video Reshooting
• Abstract summary: We create a framework capable of leveraging internet-scale monocular videos.<n>Our core contribution is the generation of pseudo multi-view training triplets, consisting of a source video, a geometric anchor, and a target video.<n>At inference, our diffusion transformer utilizes a 4D point-cloud derived anchor to achieve state-of-the-art temporal consistency.
• Score: 3.1328424544428852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Precise camera control for reshooting dynamic videos is bottlenecked by the severe scarcity of paired multi-view data for non-rigid scenes. We overcome this limitation with a highly scalable self-supervised framework capable of leveraging internet-scale monocular videos. Our core contribution is the generation of pseudo multi-view training triplets, consisting of a source video, a geometric anchor, and a target video. We achieve this by extracting distinct smooth random-walk crop trajectories from a single input video to serve as the source and target views. The anchor is synthetically generated by forward-warping the first frame of the source with a dense tracking field, which effectively simulates the distorted point-cloud inputs expected at inference. Because our independent cropping strategy introduces spatial misalignment and artificial occlusions, the model cannot simply copy information from the current source frame. Instead, it is forced to implicitly learn 4D spatiotemporal structures by actively routing and re-projecting missing high-fidelity textures across distinct times and viewpoints from the source video to reconstruct the target. At inference, our minimally adapted diffusion transformer utilizes a 4D point-cloud derived anchor to achieve state-of-the-art temporal consistency, robust camera control, and high-fidelity novel view synthesis on complex dynamic scenes.

Related papers

• ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video [51.951128822418774]
  ReViV is a framework for holistic egocentric 4D reconstruction.<n>Powered by a Masked Generative Egocentric Transformer, ReViV operates within a single feed-forward architecture.
  arXiv  Detail & Related papers  (2026-07-20T10:22:53Z)
• GeoStream: Toward Precise Camera Controlled Streaming Video Generation [85.01823220801256]
  Existing approaches learn camera motion implicitly, leading to inaccurate control under out-of-distribution trajectories.<n>Explicit geometric conditioning improves controllability, but existing methods are non-autoregressive.<n>We propose GeoStream, a framework that enables precise metric-scale camera control in autoregressive streaming video generation.
  arXiv  Detail & Related papers  (2026-06-13T07:18:04Z)
• Full-4D: Generating Full-Scope 4D Scenes from a Single-View Video [63.61099683012546]
  We introduce RealMV-4D, a dataset of synchronized multi-view videos.<n>We then train a multi-view video diffusion model driven by a novel fused time(T)-view(V) attention mechanism.<n>Experiments demonstrate that our method outperforms existing approaches in both visual fidelity and consistency.
  arXiv  Detail & Related papers  (2026-05-25T06:59:26Z)
• Diff4Splat: Controllable 4D Scene Generation with Latent Dynamic Reconstruction Models [79.06910348413861]
  We introduce Diff4Splat, a feed-forward method that synthesizes controllable and explicit 4D scenes from a single image.<n>Given a single input image, a camera trajectory, and an optional text prompt, Diff4Splat directly predicts a deformable 3D Gaussian field that encodes appearance, geometry, and motion.
  arXiv  Detail & Related papers  (2025-11-01T11:16:25Z)
• GaVS: 3D-Grounded Video Stabilization via Temporally-Consistent Local Reconstruction and Rendering [54.489285024494855]
  Video stabilization is pivotal for video processing, as it removes unwanted shakiness while preserving the original user motion intent.<n>Existing approaches, depending on the domain they operate, suffer from several issues that degrade the user experience.<n>We introduce textbfGaVS, a novel 3D-grounded approach that reformulates video stabilization as a temporally-consistent local reconstruction and rendering' paradigm.
  arXiv  Detail & Related papers  (2025-06-30T15:24:27Z)
• SpatialCrafter: Unleashing the Imagination of Video Diffusion Models for Scene Reconstruction from Limited Observations [44.53106180688135]
  This work takes on the challenge of reconstructing 3D scenes from sparse or single-view inputs.<n>We introduce SpatialCrafter, a framework that leverages the rich knowledge in video diffusion models to generate plausible additional observations.<n>Through a trainable camera encoder and an epipolar attention mechanism for explicit geometric constraints, we achieve precise camera control and 3D consistency.
  arXiv  Detail & Related papers  (2025-05-17T13:05:13Z)
• ACT-R: Adaptive Camera Trajectories for Single View 3D Reconstruction [16.03389355810877]
  We introduce the simple idea of adaptive view planning to multi-view synthesis.<n>We generate a sequence of views, leveraging temporal consistency to enhance 3D coherence.
  arXiv  Detail & Related papers  (2025-05-13T05:31:59Z)
• Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion [52.0192865857058]
  We propose the first training-free 4D video generation method that leverages the off-the-shelf video diffusion models to generate multi-view videos from a single input video.<n>Our method is training-free and fully utilizes an off-the-shelf video diffusion model, offering a practical and effective solution for multi-view video generation.
  arXiv  Detail & Related papers  (2025-03-28T17:14:48Z)
• Consistent4D: Consistent 360{\deg} Dynamic Object Generation from Monocular Video [15.621374353364468]
  Consistent4D is a novel approach for generating 4D dynamic objects from uncalibrated monocular videos. We cast the 360-degree dynamic object reconstruction as a 4D generation problem, eliminating the need for tedious multi-view data collection and camera calibration.
  arXiv  Detail & Related papers  (2023-11-06T03:26:43Z)

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.