Summary
Align4D is a flexible framework that converts any-modal input (X-to-4D) into coherent video-3D pairs, where video guides 4D motion and 3D data shapes 4D geometry. The paper introduces three key techniques: Object Distance Alignment, Motion-Geometry Joint Alignment, and Asynchronous Optimization, addressing how heterogeneous input modalities can be consistently fused for 4D generation. To support benchmarking, the authors also propose the X4D dataset. The work is from cs.CV researchers Qiaowei Miao, Kehan Li, Yawei Luo, and Yi Yang, published on arXiv (2607.02516). This framework offers a unified approach to 4D content generation by aligning motion and geometry signals from arbitrary sources rather than relying on a single modality.
Paper Overview
Research Area: cs.CV
Authors: Qiaowei Miao, Kehan Li, Yawei Luo, Yi Yang
Published: 2026-07-02
arXiv: 2607.02516
Abstract
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.
Links
- arXiv page: <https://arxiv.org/abs/2607.02516>
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