WorldStereo: Bridging Camera-Guided Video Generation and Scene Reconstruction via 3D Geometric Memories

• URL: http://arxiv.org/abs/2603.02049v1
• Date: Mon, 02 Mar 2026 16:36:56 GMT
• Title: WorldStereo: Bridging Camera-Guided Video Generation and Scene Reconstruction via 3D Geometric Memories
• Authors: Yisu Zhang, Chenjie Cao, Tengfei Wang, Xuhui Zuo, Junta Wu, Jianke Zhu, Chunchao Guo,
• Abstract summary: WorldStereo is a novel framework that bridges camera-guided video generation and 3D reconstruction.<n>We show that WorldStereo acts as a powerful world model, tackling diverse scene generation tasks with high-fidelity 3D results.
• Score: 36.79437857022868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in foundational Video Diffusion Models (VDMs) have yielded significant progress. Yet, despite the remarkable visual quality of generated videos, reconstructing consistent 3D scenes from these outputs remains challenging, due to limited camera controllability and inconsistent generated content when viewed from distinct camera trajectories. In this paper, we propose WorldStereo, a novel framework that bridges camera-guided video generation and 3D reconstruction via two dedicated geometric memory modules. Formally, the global-geometric memory enables precise camera control while injecting coarse structural priors through incrementally updated point clouds. Moreover, the spatial-stereo memory constrains the model's attention receptive fields with 3D correspondence to focus on fine-grained details from the memory bank. These components enable WorldStereo to generate multi-view-consistent videos under precise camera control, facilitating high-quality 3D reconstruction. Furthermore, the flexible control branch-based WorldStereo shows impressive efficiency, benefiting from the distribution matching distilled VDM backbone without joint training. Extensive experiments across both camera-guided video generation and 3D reconstruction benchmarks demonstrate the effectiveness of our approach. Notably, we show that WorldStereo acts as a powerful world model, tackling diverse scene generation tasks (whether starting from perspective or panoramic images) with high-fidelity 3D results. Models will be released.

Related papers

• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories [78.78355829813793]
  Existing memory-based approaches often condition generation on globally reconstructed 3D scenes by rendering anchor videos from the reconstructed geometry in the history.<n>We introduce AnchorWeave, a memory-augmented video generation framework that replaces a single misaligned global memory with multiple clean local geometric memories.<n>Experiments demonstrate that AnchorWeave significantly improves long-term scene consistency while maintaining strong visual quality.
  arXiv  Detail & Related papers  (2026-02-16T17:23:08Z)
• Beyond Inpainting: Unleash 3D Understanding for Precise Camera-Controlled Video Generation [21.084121261693365]
  We propose DepthDirector, a video re-rendering framework with precise camera controllability.<n>By leveraging the depth video from explicit 3D representation as camera-control guidance, our method can faithfully reproduce the dynamic scene of an input video under novel camera trajectories.
  arXiv  Detail & Related papers  (2026-01-15T09:26:45Z)
• Infinite-Homography as Robust Conditioning for Camera-Controlled Video Generation [49.12018869332346]
  InfCam is a camera-controlled video-to-video generation framework with high pose fidelity.<n>The framework integrates two key components: (1) infinite homography warping, which encodes 3D camera rotations directly within the 2D latent space of a video diffusion model.
  arXiv  Detail & Related papers  (2025-12-18T20:03:05Z)
• Matrix-3D: Omnidirectional Explorable 3D World Generation [20.568791715708134]
  We propose Matrix-3D, a framework that utilize panoramic representation for wide-coverage omnidirectional 3D world generation.<n>We first train a trajectory-guided panoramic video diffusion model that employs scene mesh renders as condition.<n>To lift the panorama scene video to 3D world, we propose two separate methods: (1) a feed-forward large panorama reconstruction model for rapid 3D scene reconstruction and (2) an optimization-based pipeline for accurate and detailed 3D scene reconstruction.
  arXiv  Detail & Related papers  (2025-08-11T15:29:57Z)
• Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation [66.95956271144982]
  We present Voyager, a novel video diffusion framework that generates world-consistent 3D point-cloud sequences from a single image.<n>Unlike existing approaches, Voyager achieves end-to-end scene generation and reconstruction with inherent consistency across frames.
  arXiv  Detail & Related papers  (2025-06-04T17:59:04Z)
• 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)
• Wonderland: Navigating 3D Scenes from a Single Image [43.99037613068823]
  We introduce a large-scale reconstruction model that leverages latents from a video diffusion model to predict 3D Gaussian Splattings of scenes in a feed-forward manner.<n>We train the 3D reconstruction model to operate on the video latent space with a progressive learning strategy, enabling the efficient generation of high-quality, wide-scope, and generic 3D scenes.
  arXiv  Detail & Related papers  (2024-12-16T18:58:17Z)
• LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors [107.83398512719981]
  Single-image 3D reconstruction remains a fundamental challenge in computer vision.<n>Recent advances in Latent Video Diffusion Models offer promising 3D priors learned from large-scale video data.<n>We propose LiftImage3D, a framework that effectively releases LVDMs' generative priors while ensuring 3D consistency.
  arXiv  Detail & Related papers  (2024-12-12T18:58:42Z)
• Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention [62.2447324481159]
  Cavia is a novel framework for camera-controllable, multi-view video generation. Our framework extends the spatial and temporal attention modules, improving both viewpoint and temporal consistency. Cavia is the first of its kind that allows the user to specify distinct camera motion while obtaining object motion.
  arXiv  Detail & Related papers  (2024-10-14T17:46:32Z)
• MagicDrive3D: Controllable 3D Generation for Any-View Rendering in Street Scenes [72.02827211293736]
  MagicDrive3D is a novel framework for controllable 3D street scene generation.<n>It supports multi-condition control, including road maps, 3D objects, and text descriptions.<n>It generates diverse, high-quality 3D driving scenes, supports any-view rendering, and enhances downstream tasks like BEV segmentation.
  arXiv  Detail & Related papers  (2024-05-23T12:04:51Z)
• MagicDrive: Street View Generation with Diverse 3D Geometry Control [82.69871576797166]
  We introduce MagicDrive, a novel street view generation framework, offering diverse 3D geometry controls. Our design incorporates a cross-view attention module, ensuring consistency across multiple camera views.
  arXiv  Detail & Related papers  (2023-10-04T06:14:06Z)

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.