CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation
CineOrchestra is a unified video diffusion model that simultaneously controls subjects, events, cameras, and shot transitions via entity-centric conditioning primitives and two parameter-free coordinated rotary embeddings. It outperforms six per-axis specialists on two new benchmarks.
[2606.13768] CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation
[Submitted on 11 Jun 2026]
Title:CineOrchestra: Unified Entity-Centric Conditioning for Cinematic Video Generation
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Abstract:Cinematic video depicts multiple subjects acting or interacting at specific moments, captured with deliberate camera movement, and stitched together by shot transitions. Together, these elements demand a level of fine-grained control beyond current text-to-video models. Existing work addresses each axis in isolation: multi-subject personalization, temporal control, multi-shot synthesis, or camera control; no prior framework jointly integrates all four. We present CineOrchestra, a unified video diffusion model that controls subjects, events, cameras, and shot transitions simultaneously. Our key insight is that these heterogeneous cinematic elements share a fundamental structure: each is an entity acting over a specific temporal interval, which can therefore all be expressed through one shared structure of entity-centric conditioning primitives, augmented with reference images for visual entities. This formulation reduces the architectural challenge to a single positional encoding problem, which we solve with two parameter-free coordinated rotary embeddings: (a) an interval-sampled temporal RoPE that yields consistent attention behavior across events of dramatically varying duration, and (b) a 2D entity-temporal cross-attention RoPE that disambiguates per-entity conditions and routes each to its corresponding spatiotemporal region. On two new benchmarks, CineOrchestra outperforms six per-axis specialists on dense caption following and shot-transition timing, with consistent gains in a pairwise user study and component ablations.
Comments: Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.13768 [cs.CV]
(or arXiv:2606.13768v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.13768
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Sharath Girish [view email] [v1] Thu, 11 Jun 2026 17:59:10 UTC (6,486 KB)
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