Computer Science > Computer Vision and Pattern Recognition
arXiv:2603.08850 (cs)
[Submitted on 9 Mar 2026]
Title:HECTOR: Hybrid Editable Compositional Object References for Video Generation
Authors:Guofeng Zhang, Angtian Wang, Jacob Zhiyuan Fang, Liming Jiang, Haotian Yang, Alan Yuille, Chongyang Ma
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Abstract:Real-world videos naturally portray complex interactions among distinct physical objects, effectively forming dynamic compositions of visual elements. However, most current video generation models synthesize scenes holistically and therefore lack mechanisms for explicit compositional manipulation. To address this limitation, we propose HECTOR, a generative pipeline that enables fine-grained compositional control. In contrast to prior methods,HECTOR supports hybrid reference conditioning, allowing generation to be simultaneously guided by static images and/or dynamic videos. Moreover, users can explicitly specify the trajectory of each referenced element, precisely controlling its location, scale, and speed (see Figure1). This design allows the model to synthesize coherent videos that satisfy complex spatiotemporal constraints while preserving high-fidelity adherence to references. Extensive experiments demonstrate that HECTOR achieves superior visual quality, stronger reference preservation, and improved motion controllability compared with existing approaches.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2603.08850 [cs.CV] |
| (or arXiv:2603.08850v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08850
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View a PDF of the paper titled HECTOR: Hybrid Editable Compositional Object References for Video Generation, by Guofeng Zhang and 6 other authors
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