[Submitted on 22 Oct 2026]
Title:SyncVid: World-Equivariant Diffusion for Synchronized Video Generation
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Abstract:Generating multiple videos of a dynamic scene from a single reference video requires synthesizing both observed and unobserved content consistently across viewpoints. Existing camera-control methods for video diffusion typically rely on architectural mechanisms, including camera embeddings, cross-view attention, and geometric conditioning. Although effective for static scene content, these approaches often struggle with the fine-grained, dynamic geometry that must remain coherent across views and over time. We synchronize the diffusion process itself by sampling the Gaussian noise prior in a canonical 3D space derived from the source video and rasterizing it into target cameras, rather than sampling independently in each 2D image plane. Our method, SyncVid, lifts a monocular video into a canonical 4D representation, associates noise with scene geometry and unseen space, and rasterizes the resulting structured noise into arbitrary views. Because rendering can introduce local correlations, we further propose a cross-view-consistent whitening procedure that decorrelates neighboring pixels while preserving correspondence across views. The structured noise serves as the diffusion prior for a LoRA-adapted pretrained video diffusion model, requiring no architectural modifications. By enforcing spatiotemporal and cross-view coherence at the source of stochasticity, SyncVid generates synchronized videos and improves both perceptual quality and cross-view consistency in our experiments. Project page: this https URL.
Comments: 28 pages, 8 figures, 9 tables. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2610.31765 [cs.CV]
(or arXiv:2610.31765v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.31765
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Animesh Karnewar [view email] [v1] Thu, 22 Oct 2026 18:04:12 UTC (32,372 KB)
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