Browsing by Author "Liang, Xiaohui"

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    Aesthetic Enhancement via Color Area and Location Awareness
    (The Eurographics Association, 2022) Yang, Bailin; Wang, Qingxu; Li, Frederick W. B.; Liang, Xiaohui; Wei, Tianxiang; Zhu, Changrui; Yang, Yin; Parakkat, Amal D.; Deng, Bailin; Noh, Seung-Tak
    Choosing a suitable color palette can typically improve image aesthetic, where a naive way is choosing harmonious colors from some pre-defined color combinations in color wheels. However, color palettes only consider the usage of color types without specifying their amount in an image. Also, it is still challenging to automatically assign individual palette colors to suitable image regions for maximizing image aesthetic quality. Motivated by these, we propose to construct a contribution-aware color palette from images with high aesthetic quality, enabling color transfer by matching the coloring and regional characteristics of an input image. We hence exploit public image datasets, extracting color composition and embedded color contribution features from aesthetic images to generate our proposed color palettes. We consider both image area ratio and image location as the color contribution features to extract. We have conducted quantitative experiments to demonstrate that our method outperforms existing methods through SSIM (Structural SIMilarity) and PSNR (Peak Signal to Noise Ratio) for objective image quality measurement and no-reference image assessment (NIMA) for image aesthetic scoring.
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    A Differential Diffusion Theory for Participating Media
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Cen, Yunchi; Li, Chen; Li, Frederick W. B.; Yang, Bailin; Liang, Xiaohui; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    We present a novel approach to differentiable rendering for participating media, addressing the challenge of computing scene parameter derivatives. While existing methods focus on derivative computation within volumetric path tracing, they fail to significantly improve computational performance due to the expensive computation of multiply-scattered light. To overcome this limitation, we propose a differential diffusion theory inspired by the classical diffusion equation. Our theory enables real-time computation of arbitrary derivatives such as optical absorption, scattering coefficients, and anisotropic parameters of phase functions. By solving derivatives through the differential form of the diffusion equation, our approach achieves remarkable speed gains compared to Monte Carlo methods. This marks the first differentiable rendering framework to compute scene parameter derivatives based on diffusion approximation. Additionally, we derive the discrete form of diffusion equation derivatives, facilitating efficient numerical solutions. Our experimental results using synthetic and realistic images demonstrate the accurate and efficient estimation of arbitrary scene parameter derivatives. Our work represents a significant advancement in differentiable rendering for participating media, offering a practical and efficient solution to compute derivatives while addressing the limitations of existing approaches.
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    Modeling Detailed Cloud Scene from Multi-source Images
    (The Eurographics Association, 2018) Cen, Yunchi; Liang, Xiaohui; Chen, Junping; Yang, Bailin; Li, Frederick W. B.; Fu, Hongbo and Ghosh, Abhijeet and Kopf, Johannes
    Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For example, natural images are usually used to model small-scale cloud with details, and satellite images and WRF data are used to model large scale cloud without details. To construct large-scale cloud scene with details, we propose a novel method to extract relevant cloud information from both satellite and natural images. Experiments show our method can produce more detailed cloud scene comparing with existing methods.