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Sora Generates Videos with Stunning Geometrical Consistency

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arxiv 2402.17403 v1 pith:2M3GEUGR submitted 2024-02-27 cs.CV

classification cs.CV
keywords real-worldvideosgeneratedphysicsfidelitymodelsqualityreconstruction
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The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena. Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively. In this paper, we introduce a new benchmark that assesses the quality of the generated videos based on their adherence to real-world physics principles. We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality. From the perspective of 3D reconstruction, we use the fidelity of the geometric constraints satisfied by the constructed 3D models as a proxy to gauge the extent to which the generated videos conform to real-world physics rules. Project page: https://sora-geometrical-consistency.github.io/

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors

    cs.CV 2024-12 conditional novelty 6.0 of 10

    LiftImage3D generates small-motion video clips from one image, registers them with MASt3R, and fits a distortion-aware 3D Gaussian field whose canonical scene renders new views.

  2. Mapping the Mind of an Instruction-based Image Editing using SMILE

    cs.AI 2024-12 reject novelty 4.0 of 10

    SMILE applies LIME-style prompt perturbation with image-embedding distances to create word-level heatmaps for instruction-based image editing models.

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