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. Align4D introduces three key techniques: (1) Object Distance Alignment, which searches Video-Aligned and Multivi
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: luo”
- LinkedLinked via arxiv author · 85%Qiaowei Miao →
“Alignment Is All You Need For X-to-4D Generation”
- LinkedLinked via arxiv author · 85%Kehan Li →
“Alignment Is All You Need For X-to-4D Generation”
- LinkedLinked via arxiv author · 85%Yawei Luo →
“Alignment Is All You Need For X-to-4D Generation”
- LinkedLinked via arxiv author · 85%Yi Yang →
“Alignment Is All You Need For X-to-4D Generation”