Adaptive Collage-Based Image Generation: A Framework for Dynamic Visual Composition Through Autonomous Fragment Selection, Transformation, and Integration
Abstract
Contemporary image generation systems predominantly synthesize images through learned latent representations, iterative denoising, autoregressive prediction, or combinations of these mechanisms. Although these approaches can produce highly detailed images, explicitly controlling the selection, placement, transformation, and interaction of heterogeneous visual fragments remains a distinct problem in compositional image generation. This paper proposes Adaptive Collage-Based Image Generation (ACIG), a framework that represents image construction as a sequence of operations on discrete, editable visual fragments. Rather than treating the image exclusively as a continuous field of pixels or latent variables, ACIG models a canvas as an evolving composition of fragments that can be selected, generated, cropped, transformed, layered, blended, replaced, or removed. The framework introduces a fragment-state representation, a dynamic composition controller, a multi-component quality objective, and an optional reinforcement learning mechanism for sequential decision-making. Fragment cardinality is allowed to change throughout generation, enabling the composition to adapt its representational complexity to the target image. The proposed objective balances semantic alignment, structural coherence, visual compatibility, boundary quality, computational cost, and unnecessary fragment complexity. An experimental protocol is developed to evaluate the framework against conventional collage algorithms, fixed-cardinality composition, heuristic fragment placement, and pretrained image generation systems. This paper presents a formal research framework rather than an experimentally validated implementation. Its contribution is the specification and integration of dynamic fragment-level composition, adaptive editing, and cost-aware decision-making into a unified image generation formulation. The extent of its novelty and practical advantages must be established through implementation, comparative experiments, and systematic evaluation against existing collage-generation and compositional synthesis methods.
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BibTeX
@misc{zhang2026adaptive,
title = {{Adaptive Collage-Based Image Generation: A Framework for Dynamic Visual Composition Through Autonomous Fragment Selection, Transformation, and Integration}},
author = {Jincheng Zhang},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23555839},
url = {https://zenodo.org/records/23555839},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Adaptive Collage-Based Image Generation: A Framework for Dynamic Visual Composition Through Autonomous Fragment Selection, Transformation, and Integration
AU - Jincheng Zhang
PY - 2026
DA - 2026/10/22
PB - Zenodo
DO - 10.5281/zenodo.23555839
UR - https://zenodo.org/records/23555839
AB - Contemporary image generation systems predominantly synthesize images through learned latent representations, iterative denoising, autoregressive prediction, or combinations of these mechanisms. Although these approaches can produce highly detailed images, explicitly controlling the selection, placement, transformation, and interaction of heterogeneous visual fragments remains a distinct problem in compositional image generation. This paper proposes Adaptive Collage-Based Image Generation (ACIG), a framework that represents image construction as a sequence of operations on discrete, editable visual fragments. Rather than treating the image exclusively as a continuous field of pixels or latent variables, ACIG models a canvas as an evolving composition of fragments that can be selected, generated, cropped, transformed, layered, blended, replaced, or removed. The framework introduces a fragment-state representation, a dynamic composition controller, a multi-component quality objective, and an optional reinforcement learning mechanism for sequential decision-making. Fragment cardinality is allowed to change throughout generation, enabling the composition to adapt its representational complexity to the target image. The proposed objective balances semantic alignment, structural coherence, visual compatibility, boundary quality, computational cost, and unnecessary fragment complexity. An experimental protocol is developed to evaluate the framework against conventional collage algorithms, fixed-cardinality composition, heuristic fragment placement, and pretrained image generation systems. This paper presents a formal research framework rather than an experimentally validated implementation. Its contribution is the specification and integration of dynamic fragment-level composition, adaptive editing, and cost-aware decision-making into a unified image generation formulation. The extent of its novelty and practical advantages must be established through implementation, comparative experiments, and systematic evaluation against existing collage-generation and compositional synthesis methods.
ER -