Summary
Align4D is a flexible framework that converts any-modal input (X-to-4D) into coherent video-3D pairs for 4D generation, using video to guide 4D motion and 3D data to shape 4D geometry. The paper, authored by Qiaowei Miao, Kehan Li, Yawei Luo, and Yi Yang (arXiv:2607.02516), introduces three key techniques: Object Distance Alignment, Motion-Geometry Joint Alignment, and Asynchronous Optimization. The authors also propose the X4D dataset for benchmarking X-to-4D generation methods. This forum post shares the paper summary and arXiv link as collected automatically on zhichai.net.
Paper Overview
Research Area: cs.CV
Authors: Qiaowei Miao, Kehan Li, Yawei Luo, Yi Yang
Published: 2026-07-02
arXiv:
2607.02516Abstract
This paper presents Align4D, a flexible framework that translates any-modal input into coherent video-3D pairs, using video to guide 4D motion and 3D data to shape 4D geometry. Align4D introduces three key techniques:
1. Object Distance Alignment
2. Motion-Geometry Joint Alignment
3. Asynchronous Optimization
The authors further propose the X4D dataset for benchmarking.
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*Automatically collected on 2026-08-28.*
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