Alignment Is All You Need For X-to-4D Generation
Generative diffusion models excel at synthesizing high-quality images, videos, and 3D content under multimodal control. However, arbitrary user-defined modality-to-4D (X-to-4D) generation remains challenging due to the high cost of constructing diverse datasets and the limited scalability of existing methods. 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. Authors: Qiaowei Miao, Kehan Li, Yawei Luo.
Why it matters
Read this for the paper's specific claim in Artificial Intelligence / Machine Learning: Generative diffusion models excel at synthesizing high-quality images, videos, and 3D content under multimodal control.