@inproceedings{li-etal-2026-decomposing,
title = "Decomposing Visual Histories with Vision-Language Agents: Hierarchical Temporal Guidance for Compositional Image Generation",
author = "Li, Lujundong and
Zhang, Mingxu and
Lai, Songning and
Yang, Jiayu and
Fan, Yuxuan and
Xie, Ziwei and
Liu, Feng and
Zhang, Changwang and
Wang, Jun and
Shen, Dazhong and
Sun, Ying",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.607/",
pages = "13989--14000",
ISBN = "979-8-89176-459-0",
abstract = "Reference-conditioned image generation increasingly relies on visual histories that rarely speak with one voice: one example pins the subject, another fixes a layout, a third only suggests a palette. Current image-conditioned diffusion methods encode these references once, average them into a single vector, and inject it at every denoising step, so conflicting cues collide and the output is faithful to none. We propose the Hierarchical Temporal Guidance Framework (HTGF), a training-free pipeline that reframes multi-reference conditioning as a *temporal routing* problem. A semantic decomposer reads each reference and emits a soft routing distribution over three axes (Subject, Structure, Detail), and a closed-form SNR-sensitivity argument places each axis in the denoising window where it has the most leverage. A short manifold-aware corrector then smooths the trajectory at the stage boundaries where the active condition changes. HTGF adds no training and no architectural change to the diffusion backbone. On three datasets it outperforms strong VLM- and diffusion-based baselines, gaining $+11.7$ CIS over LaVIT on Movie Poster and FID 38.87 vs. 50.12 for the best baseline under noisy histories, and degrading gracefully down to the zero-reference limit."
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<abstract>Reference-conditioned image generation increasingly relies on visual histories that rarely speak with one voice: one example pins the subject, another fixes a layout, a third only suggests a palette. Current image-conditioned diffusion methods encode these references once, average them into a single vector, and inject it at every denoising step, so conflicting cues collide and the output is faithful to none. We propose the Hierarchical Temporal Guidance Framework (HTGF), a training-free pipeline that reframes multi-reference conditioning as a *temporal routing* problem. A semantic decomposer reads each reference and emits a soft routing distribution over three axes (Subject, Structure, Detail), and a closed-form SNR-sensitivity argument places each axis in the denoising window where it has the most leverage. A short manifold-aware corrector then smooths the trajectory at the stage boundaries where the active condition changes. HTGF adds no training and no architectural change to the diffusion backbone. On three datasets it outperforms strong VLM- and diffusion-based baselines, gaining +11.7 CIS over LaVIT on Movie Poster and FID 38.87 vs. 50.12 for the best baseline under noisy histories, and degrading gracefully down to the zero-reference limit.</abstract>
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%0 Conference Proceedings
%T Decomposing Visual Histories with Vision-Language Agents: Hierarchical Temporal Guidance for Compositional Image Generation
%A Li, Lujundong
%A Zhang, Mingxu
%A Lai, Songning
%A Yang, Jiayu
%A Fan, Yuxuan
%A Xie, Ziwei
%A Liu, Feng
%A Zhang, Changwang
%A Wang, Jun
%A Shen, Dazhong
%A Sun, Ying
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-decomposing
%X Reference-conditioned image generation increasingly relies on visual histories that rarely speak with one voice: one example pins the subject, another fixes a layout, a third only suggests a palette. Current image-conditioned diffusion methods encode these references once, average them into a single vector, and inject it at every denoising step, so conflicting cues collide and the output is faithful to none. We propose the Hierarchical Temporal Guidance Framework (HTGF), a training-free pipeline that reframes multi-reference conditioning as a *temporal routing* problem. A semantic decomposer reads each reference and emits a soft routing distribution over three axes (Subject, Structure, Detail), and a closed-form SNR-sensitivity argument places each axis in the denoising window where it has the most leverage. A short manifold-aware corrector then smooths the trajectory at the stage boundaries where the active condition changes. HTGF adds no training and no architectural change to the diffusion backbone. On three datasets it outperforms strong VLM- and diffusion-based baselines, gaining +11.7 CIS over LaVIT on Movie Poster and FID 38.87 vs. 50.12 for the best baseline under noisy histories, and degrading gracefully down to the zero-reference limit.
%U https://aclanthology.org/2026.emnlp-main.607/
%P 13989-14000
Markdown (Informal)
[Decomposing Visual Histories with Vision-Language Agents: Hierarchical Temporal Guidance for Compositional Image Generation](https://aclanthology.org/2026.emnlp-main.607/) (Li et al., EMNLP 2026)
ACL
- Lujundong Li, Mingxu Zhang, Songning Lai, Jiayu Yang, Yuxuan Fan, Ziwei Xie, Feng Liu, Changwang Zhang, Jun Wang, Dazhong Shen, and Ying Sun. 2026. Decomposing Visual Histories with Vision-Language Agents: Hierarchical Temporal Guidance for Compositional Image Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 13989–14000, Budapest, Hungary. Association for Computational Linguistics.