Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation

• URL: http://arxiv.org/abs/2607.05354v1
• Date: Mon, 06 Jul 2026 17:32:04 GMT
• Title: Geometric Reciprocity: Unlocking Self-Supervision for Stereoscopic Video Generation
• Abstract summary: Training-based methods achieve superior quality but rely on scarce stereo pairs or synthetic data with domain gaps.<n>We address this through the first self-supervised framework learning from monocular videos via cycle consistency.<n>This yields train-test consistency for the stated warping formulation, supporting self-supervised learning from unlimited monocular videos.
• Score: 17.05562746375872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular-to-stereo conversion synthesizes stereoscopic content from 2D videos for immersive 3D experiences. In modern Depth-Image-Based Rendering (DIBR) approaches, stereo inpainting of disocclusions is the critical bottleneck. Training-based methods achieve superior quality but rely on scarce stereo pairs or synthetic data with domain gaps. We address this through the first self-supervised framework learning from monocular videos via cycle consistency. Our key contribution is the Geometric Reciprocity Theorem (GRT): under the nearest-neighbor DIBR formulation, the disocclusion mask when synthesizing a target view equals the mask of pixels lost when warping back from target to source, enabling analytical computation of test-time disocclusion masks directly from monocular images. This yields train-test consistency for the stated warping formulation, supporting self-supervised learning from unlimited monocular videos and substantial improvements over training-free and supervised state-of-the-art methods. Project page: https://visual-ai.github.io/grt/

Related papers

• GenRec: Knowing Where to Reconstruct and Where to Generate [106.48347271421765]
  GenRec is a flow matching model that builds the reconstruction--generation split directly into its architecture, supervision, and gradient flow.<n>It attains the best reconstruction fidelity in observed regions while also surpassing purely generative baselines on perceptual quality in unobserved ones.
  arXiv  Detail & Related papers  (2026-08-18T14:31:19Z)
• Visual Geometry Transformer in the Wild: Distractor-Free 3D Reconstruction [77.75911117889915]
  Current end-to-end multi-view 3D reconstruction methods achieve impressive results, but rely on a restrictive static assumption.<n>This reliance on idealized inputs causes even the most advanced methods to fail in real-world settings.<n>We propose Visual Geometry Transformer in the Wild, an end-to-end framework for robust reconstruction from inconsistent views.
  arXiv  Detail & Related papers  (2026-06-22T02:52:22Z)
• GeoSAM-3D: Geodesic Prompt Propagation for Open-Vocabulary 3D Scene Segmentation from Monocular Video [1.5229257192293202]
  GeoSAM-3D studies a lighter setting: a user uploads a short monocular video, clicks or names an object in one frame, and receives a propagated 3D mask over a Gaussian scene.<n>The implementation combines frozen image and video foundation models with a monocular 3D Gaussian Splatting reconstruction and a differentiable graph-geodesic propagation kernel over Gaussian centroids.<n>This paper describes the repository state, the mathematical kernel implemented in geosam3d.propagate, the feature head trained from Segment Anything masks, and the validation already present in the scene.
  arXiv  Detail & Related papers  (2026-05-30T00:34:37Z)
• StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception [30.791524912385924]
  StereoPolicy is a visuomotor policy learning framework that directly leverages synchronized stereo image pairs to strengthen geometric reasoning.<n>Our results underscore stereo vision as a scalable and robust modality that bridges 2D pretrained representations with 3D geometric understanding for robotic manipulation.
  arXiv  Detail & Related papers  (2026-05-11T05:06:12Z)
• UMAMI: Unifying Masked Autoregressive Models and Deterministic Rendering for View Synthesis [28.245380116188883]
  Novel view synthesis (NVS) seeks to render photorealistic, 3D-consistent images of a scene from unseen camera poses given only a sparse set of posed views.<n>Existing deterministic networks render observed regions quickly but blur unobserved areas, whereas diffusion-based methods hallucinate plausible content yet incur heavy training-time costs.<n>We propose a hybrid framework that unifies the strengths of both paradigms. A bidirectional transformer encodes multi-view image tokens and Plucker-ray embeddings, producing a shared latent representation.
  arXiv  Detail & Related papers  (2025-12-23T07:08:00Z)
• S^2VG: 3D Stereoscopic and Spatial Video Generation via Denoising Frame Matrix [60.060882467801484]
  We present a pose-free and training-free method that leverages an off-the-shelf monocular video generation model to produce immersive 3D videos.<n>Our approach first warps the generated monocular video into pre-defined camera viewpoints using estimated depth information, then applies a novel textitframe matrix inpainting framework.<n>We validate the efficacy of our proposed method by conducting experiments on videos from various generative models, such as Sora, Lumiere, WALT, and Zeroscope.
  arXiv  Detail & Related papers  (2025-08-11T14:50:03Z)
• DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion [50.90541069907167]
  We propose DeOcc-1-to-3, an end-to-end framework for occlusion-aware multi-view generation.<n>Our self-supervised training pipeline leverages occluded-unoccluded image pairs and pseudo-ground-truth views to teach the model structure-aware completion and view consistency.
  arXiv  Detail & Related papers  (2025-06-26T17:58:26Z)
• Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis [45.64047250474718]
  Despite advances in video synthesis, creating 3D videos remains challenging due to the relative scarcity of 3D video data.<n>We propose a simple approach for transforming a text-to-video generator into a video-to-stereo generator.<n>Our framework automatically produces the video frames from a shifted viewpoint, enabling a compelling 3D effect.
  arXiv  Detail & Related papers  (2025-04-30T19:06:09Z)
• SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix [60.48666051245761]
  We propose a pose-free and training-free approach for generating 3D stereoscopic videos. Our method warps a generated monocular video into camera views on stereoscopic baseline using estimated video depth. We develop a disocclusion boundary re-injection scheme that further improves the quality of video inpainting.
  arXiv  Detail & Related papers  (2024-06-29T08:33:55Z)
• MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision [75.38953287579616]
  We present a novel framework to exploit Multi-view Occlusion-aware supervision from hand-object videos for Hand-held Object reconstruction. We tackle two predominant challenges in such setting: hand-induced occlusion and object's self-occlusion. Experiments on HO3D and DexYCB datasets demonstrate 2D-supervised MOHO gains superior results against 3D-supervised methods by a large margin.
  arXiv  Detail & Related papers  (2023-10-18T03:57:06Z)
• Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion [45.171150395915056]
  3D semantic scene completion (SSC) is an ill-posed perception task that requires inferring a dense 3D scene from limited observations. Previous camera-based methods struggle to predict accurate semantic scenes due to inherent geometric ambiguity and incomplete observations. We resort to stereo matching technique and bird's-eye-view (BEV) representation learning to address such issues in SSC.
  arXiv  Detail & Related papers  (2023-03-24T12:33:44Z)
• Learning 3D Photography Videos via Self-supervised Diffusion on Single Images [105.81348348510551]
  3D photography renders a static image into a video with appealing 3D visual effects. Existing approaches typically first conduct monocular depth estimation, then render the input frame to subsequent frames with various viewpoints. We present a novel task: out-animation, which extends the space and time of input objects.
  arXiv  Detail & Related papers  (2023-02-21T16:18:40Z)

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.