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ScaleVid framework enables geometry-aware video object scaling without 3D reconstruction

Researchers have developed ScaleVid, a novel framework for geometry-aware video object scaling that aims to resize objects anisotropically while maintaining geometric plausibility and temporal coherence. This method avoids costly 3D reconstruction by employing a progressive two-stage training process that separates foreground transformation from background preservation. The framework synthesizes realistic video compositions without requiring paired real-world scaling targets, demonstrating superior geometric consistency and faster inference compared to existing approaches. AI

IMPACT Enables more precise and efficient manipulation of video content by allowing objects to be resized while maintaining visual integrity.

RANK_REASON The item is a research paper detailing a new method for video object scaling. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ScaleVid framework enables geometry-aware video object scaling without 3D reconstruction

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The item is a research paper detailing a new method for video object scaling. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Youze Huang, Penghui Ruan, Bojia Zi, Xianbiao Qi, Shihao Zhao, Rong Xiao ·

    ScaleVid: Geometry-Aware Video Object Scaling with Mesh-Free Inference

    arXiv:2608.12232v1 Announce Type: new Abstract: Geometry-aware video object scaling aims to anisotropically resize the object along object-centric axes while preserving geometric plausibility, temporal coherence, and background consistency. Existing text-guided methods mainly ope…