Browsing by Author "Wang, Miao"

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    Learning Explicit Smoothing Kernels for Joint Image Filtering
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Fang, Xiaonan; Wang, Miao; Shamir, Ariel; Hu, Shi-Min; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Smoothing noises while preserving strong edges in images is an important problem in image processing. Image smoothing filters can be either explicit (based on local weighted average) or implicit (based on global optimization). Implicit methods are usually time-consuming and cannot be applied to joint image filtering tasks, i.e., leveraging the structural information of a guidance image to filter a target image.Previous deep learning based image smoothing filters are all implicit and unavailable for joint filtering. In this paper, we propose to learn explicit guidance feature maps as well as offset maps from the guidance image and smoothing parameter that can be utilized to smooth the input itself or to filter images in other target domains. We design a deep convolutional neural network consisting of a fully-convolution block for guidance and offset maps extraction together with a stacked spatially varying deformable convolution block for joint image filtering. Our models can approximate several representative image smoothing filters with high accuracy comparable to state-of-the-art methods, and serve as general tools for other joint image filtering tasks, such as color interpolation, depth map upsampling, saliency map upsampling, flash/non-flash image denoising and RGB/NIR image denoising.
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    Write-An-Animation: High-level Text-based Animation Editing with Character-Scene Interaction
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Zhang, Jia-Qi; Xu, Xiang; Shen, Zhi-Meng; Huang, Ze-Huan; Zhao, Yang; Cao, Yan-Pei; Wan, Pengfei; Wang, Miao; Zhang, Fang-Lue and Eisemann, Elmar and Singh, Karan
    3D animation production for storytelling requires essential manual processes of virtual scene composition, character creation, and motion editing, etc. Although professional artists can favorably create 3D animations using software, it remains a complex and challenging task for novice users to handle and learn such tools for content creation. In this paper, we present Write-An- Animation, a 3D animation system that allows novice users to create, edit, preview, and render animations, all through text editing. Based on the input texts describing virtual scenes and human motions in natural languages, our system first parses the texts as semantic scene graphs, then retrieves 3D object models for virtual scene composition and motion clips for character animation. Character motion is synthesized with the combination of generative locomotions using neural state machine as well as template action motions retrieved from the dataset. Moreover, to make the virtual scene layout compatible with character motion, we propose an iterative scene layout and character motion optimization algorithm that jointly considers characterobject collision and interaction. We demonstrate the effectiveness of our system with customized texts and public film scripts. Experimental results indicate that our system can generate satisfactory animations from texts.