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New diffusion model embeds physical reasoning for realistic shadow generation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for generating realistic cast shadows in images by incorporating physical reasoning into diffusion models. This approach explicitly considers scene geometry and illumination, unlike previous methods that treated shadow generation as a simple image translation task. The system recovers approximate scene geometry and estimates dominant light directions to create a coarse shadow estimate, which then conditions a diffusion-based generator for refinement. Experiments on the DESOBAv2 dataset show significant improvements in shadow accuracy and localization, with a 23% reduction in shadow-region RMSE and a 30% decrease in shadow-mask BER compared to existing state-of-the-art techniques. AI

IMPACT This research could lead to more realistic visual effects in computer graphics and image editing applications.

RANK_REASON The cluster contains an academic paper detailing a new method for image generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New diffusion model embeds physical reasoning for realistic shadow generation

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The cluster contains an academic paper detailing a new method for image generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Shilin Hu, Jingyi Xu, Akshat Dave, Dimitris Samaras, Hieu Le ·

    Embedding Physical Reasoning into Diffusion-Based Shadow Generation Under the Sun and Sky

    arXiv:2512.06174v3 Announce Type: replace Abstract: Generating realistic cast shadows for inserted foreground objects requires reasoning about scene geometry and illumination. However, most learning-based approaches treat shadow generation as an image translation problem and capt…

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