Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning
Humans paint images incrementally: they plan a global layout, sketch a coarse draft, inspect, and refine details, and most importantly, each step is grounded in the evolving visual states. However, can unified multimodal models trained on text-image interleaved datasets also imagine the chain of intermediate states? In this paper, we introduce process-driven image generation, a multi-step paradigm that decomposes synthesis into an interleaved reasoning trajectory of thoughts and actions. Rather than generating images in a single step, our approach unfolds across multiple iterations, each consisting of 4 stages: textual planning, visual drafting, textual reflection, and visual refinement. The textual reasoning explicitly conditions how the visual state should evolve, while the generated visual intermediate in turn constrains and grounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: how can models evaluate each partially-complete image? We address this through dense, step-wise supervision that maintains two complementary constraints: for the visual intermediate states, we enforce the spatial and semantic consistency; for the textual intermediate states, we preserve the prior visual knowledge while enabling the model to identify and correct prompt-violating elements. This makes the generation process explicit, interpretable, and directly supervisable. To validate proposed method, we conduct experiments under various text-to-image generation benchmarks.
69d71a49c6ed9968ac5c69be
Lei Zhang
claimed_verified
2026-04-09T08:06:47.347Z
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/avatars/497da53d0d5485c9536538291678563b.svg
Lei Zhang
leiiizhang
user
leiiizhang
69d71a49c6ed9968ac5c69bf
Junjiao Tian
69d71a49c6ed9968ac5c69c0
Zhipeng Fan
69d71a49c6ed9968ac5c69c1
Kunpeng Li
69d71a49c6ed9968ac5c69c2
Jialiang Wang
69d71a49c6ed9968ac5c69c3
Weifeng Chen
69d71a49c6ed9968ac5c69c4
Markos Georgopoulos
69d71a49c6ed9968ac5c69c5
Felix Juefei-Xu
69d71a49c6ed9968ac5c69c6
Yuxiang Bao
69d71a49c6ed9968ac5c69c7
Julian McAuley
69d71a49c6ed9968ac5c69c8
Manling Li
claimed_verified
2026-04-13T07:34:17.055Z
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/avatars/d9d8cfb5f112e6ed7f6152fc230135d3.svg
Manling Li
ManlingLi
user
ManlingLi
69d71a49c6ed9968ac5c69c9
Zecheng He
Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning
Humans paint images incrementally: they plan a global layout, sketch a coarse draft, inspect, and refine details, and most importantly, each step is grounded in the evolving visual states. However, can unified multimodal models trained on text-image interleaved datasets also imagine the chain of intermediate states? In this paper, we introduce process-driven image generation, a multi-step paradigm that decomposes synthesis into an interleaved reasoning trajectory of thoughts and actions. Rather than generating images in a single step, our approach unfolds across multiple iterations, each consisting of 4 stages: textual planning, visual drafting, textual reflection, and visual refinement. The textual reasoning explicitly conditions how the visual state should evolve, while the generated visual intermediate in turn constrains and grounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: how can models evaluate each partially-complete image? We address this through dense, step-wise supervision that maintains two complementary constraints: for the visual intermediate states, we enforce the spatial and semantic consistency; for the textual intermediate states, we preserve the prior visual knowledge while enabling the model to identify and correct prompt-violating elements. This makes the generation process explicit, interpretable, and directly supervisable. To validate proposed method, we conduct experiments under various text-to-image generation benchmarks.
Process-driven image generation decomposes synthesis into iterative steps involving textual planning, visual drafting, textual reflection, and visual refinement, with step-wise supervision ensuring consistency and interpretability.
textual planning visual drafting textual reflection visual refinement step-wise supervision spatial consistency semantic consistency prompt-violating elements