From dc884ae2feb6992e7a29dce3a564735a28cad250 Mon Sep 17 00:00:00 2001 From: Your Name <664858757@qq.com> Date: Mon, 15 Apr 2024 21:36:51 +0800 Subject: [PATCH] updated ICME 24 --- en/projects_2Dshape.html | 81 ++++++++++++++++++++- en/publications.html | 3 + files/images/home/sketch-teasers.jpg | Bin 449774 -> 456599 bytes files/images/projects/icme24-animation.png | Bin 0 -> 165969 bytes files/images/projects/icme24-editing.gif | Bin 0 -> 463547 bytes files/images/projects/icme24-piano.png | Bin 0 -> 137171 bytes projects_2Dshape.html | 80 ++++++++++++++++++-- publications_papers.html | 3 + 8 files changed, 156 insertions(+), 11 deletions(-) create mode 100644 files/images/projects/icme24-animation.png create mode 100644 files/images/projects/icme24-editing.gif create mode 100644 files/images/projects/icme24-piano.png diff --git a/en/projects_2Dshape.html b/en/projects_2Dshape.html index 3b52c94d..cf2d46cd 100644 --- a/en/projects_2Dshape.html +++ b/en/projects_2Dshape.html @@ -59,7 +59,7 @@
  • Image Editing and Synthesis
  • 3D Pose Estimation and Motion Generation
  • Garment Modeling and Virtual Try-on
  • -
  • 3D Rendering & Modeling
  • +
  • Multimedia Processing & 3D Rendering and Modeling
  • @@ -122,6 +122,59 @@

    Joint Stroke Tracing and Corres + + + + + +

    Text-based Vector Sketch Editing with Image Editing Diffusion Prior

    +

    + Haoran Mo, Xusheng Lin, Chengying Gao* and Ruomei Wang +
    +
    + Intro: + We present a framework for text-based vector sketch editing to improve the efficiency of graphic design. + The key idea behind the approach is to transfer the prior information from raster-level diffusion models, + especially those from image editing methods, into the vector sketch-oriented task. + The framework presents three editing modes and allows iterative editing. + To meet the editing requirement of modifying the intended parts only while avoiding changing the other strokes, + we introduce a stroke-level local editing scheme that automatically produces an editing mask reflecting locally editable regions and modifies strokes within the regions only. +
    +
    + International Conference on Multimedia & Expo (ICME, 2024)  (CCF-B) +
    + [Paper] + [Code] +

    + + + + + + + + +

    Video-Driven Sketch Animation via Cyclic Reconstruction Mechanism

    +

    + Zhuo Xie, Haoran Mo and Chengying Gao* +
    +
    + Intro: + Considering the time-consuming manual workflow in 2D sketch animation production, + we present an automatic solution by using videos as reference to animate the static sketch images. + This includes motion extraction from the videos and injection into the sketches to produce animated sketch sequences in which appearance properties from the source sketches should be preserved. + To reduce blurry artifact caused by complex motions and maintain stroke line continuity, + we propose to incorporate inner masks of the sketches as an explicit guidance to indicate inner regions and ensure component integrality. + Moreover, to bridge the domain gap between the video frames and the sketches when modelling the motions, + we introduce a cyclic reconstruction mechanism to increase compatibility with different domains and improve motion consistency between the sketch animation and the driving video. +
    +
    + International Conference on Multimedia & Expo (ICME, 2024)  (CCF-B) +
    +

    + + + @@ -617,15 +670,37 @@

    Automat
    -

    3D Rendering & Modeling

    +

    Multimedia Processing & 3D Rendering and Modeling

    - Including: fast fluid surface reconstruction based on narrow band method, fabric modeling and rendering. + Multimedia Processing: generation and understanding of music and dance. +
    + 3D Rendering and Modeling: fast fluid surface reconstruction based on narrow band method, fabric modeling and rendering.


    + + + + + + + + + + + + + + +
    + + +

    Video-Driven Sketch Animation via Cyclic Reconstruction Mechanism

    +

    + Xiao Liang, Zijian Zhao, Weichao Zeng, Yutong He, Fupeng He, Yiyi Wang and Chengying Gao +
    +
    + Intro: + Learning musical structures and composition patterns is necessary for both music generation and understanding, but current methods do not make uniform use of learned features to generate and comprehend music simultaneously. In this paper, we propose PianoBART, a pre-trained model that uses BART for both symbolic piano music generation and understanding. We devise a multi-level object selection strategy for different pre-training tasks of PianoBART, which can prevent information leakage or loss and enhance learning ability. The musical semantics captured in pre-training are fine-tuned for music generation and understanding tasks. Experiments demonstrate that PianoBART efficiently learns musical patterns and achieves outstanding performance in generating high-quality coherent pieces and comprehending music. +
    +
    + International Conference on Multimedia & Expo (ICME, 2024)  (CCF-B) +
    +

    +
    diff --git a/en/publications.html b/en/publications.html index eb9e01e7..c253a8bf 100644 --- a/en/publications.html +++ b/en/publications.html @@ -58,6 +58,9 @@

    2024

      +
    1. Xiao Liang, Zijian Zhao, Weichao Zeng, Yutong He, Fupeng He, Yiyi Wang and Chengying Gao, PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training, Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2024)  (CCF-B)
    2. +
    3. Zhuo Xie, Haoran Mo and Chengying Gao*, Video-Driven Sketch Animation via Cyclic Reconstruction Mechanism, Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2024)  (CCF-B)
    4. +
    5. Haoran Mo, Xusheng Lin, Chengying Gao* and Ruomei Wang, Text-based Vector Sketch Editing with Image Editing Diffusion Prior, Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2024)  (CCF-B)
    6. Haoran Mo, Chengying Gao* and Ruomei Wang, Joint Stroke Tracing and Correspondence for 2D Animation, ACM Transactions on Graphics (Presented at SIGGRAPH 2024)  (CCF-A)

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  • 三维姿势估计与动作生成
  • 服装建模与虚拟试衣
  • -
  • 三维渲染与建模
  • +
  • 多媒体处理与三维渲染建模
  • @@ -129,12 +129,12 @@

    Joint Stroke Tracing and Corres

    简介: - 此工作提出一个基于草图笔划矢量化和关联匹配的二维动画生成方法。 - 该方法以像素草图关键帧序列与人工标注的首帧矢量草图为输入,实现为后续关键帧生成矢量笔划,并构成帧间笔划匹配关系,用于后续动画生成。 + 此工作提出一个基于草图笔画矢量化和关联匹配的二维动画生成方法。 + 该方法以像素草图关键帧序列与人工标注的首帧矢量草图为输入,实现为后续关键帧生成矢量笔画,并构成帧间笔画匹配关系,用于后续动画生成。 虽然以干净草图进行训练,模型可以泛化应用于粗糙草图。 生成的结果能够直接导入插帧软件,进行连贯序列帧的生成,构成二维动画。 - 本方法还提出一个自适应的空间变换模块(ASTM),用于处理非刚性运动和笔划扭曲。 - 另外,本工作还构建了一个包含10k+组像素关键帧及其带有笔划匹配标注的矢量图构成的数据集,用于模型的训练。 + 本方法还提出一个自适应的空间变换模块(ASTM),用于处理非刚性运动和笔画扭曲。 + 另外,本工作还构建了一个包含10k+组像素关键帧及其带有笔画匹配标注的矢量图构成的数据集,用于模型的训练。

    ACM Transactions on Graphics (Presented at SIGGRAPH 2024)  (CCF-A) @@ -146,6 +146,49 @@

    Joint Stroke Tracing and Corres

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zr#hNIy3L8aI$I97x~P$L*xmv@jV?ntuFS`&xE5p)zxurVu-a|VTG3y`@gAU1*%T6-J_bxcAn^te zy~l9@<{{wV1%%gwr7kTE4K*s{9E=Aw?ygNw<`8q7QB1y2^XCPRlYv%F6PyE7*#KZ! zdh6TGDOLeFZJDLBh~~b{C^Bb?kUj;d>e|`>)b#4;%u$U>n;1g4Wci9w+t;`8`~YcRv)%k_gvbz8 z|I=;!U$c(wmntptjzL3={~F@|+wduJwg=8ZCxisD1hH|EVP47RguzNKB{_XWGVKYmB zIv>}6_wNtIn7>c)&qu>cNVoQ%-v4}bDE$BLhG=Qf{|W(F?s50}fa4BF0skJ#J$X图像编辑与生成

    + + +

    Text-based Vector Sketch Editing with Image Editing Diffusion Prior

    +

    + Haoran Mo, Xusheng Lin, Chengying Gao* and Ruomei Wang +
    +
    + 简介: + 本文提出了一个基于文本的矢量草图编辑框架,来提高图形设计的效率。该方法的关键思想是提取像素级图像编辑扩散模型的先验知识,并迁移到矢量草图编辑任务上。 + 该框架支持3种编辑模式,并允许进行迭代式编辑。为了满足仅调整目标区域,而不改动其他笔画的需求,本文设计了一个局部区域笔画编辑机制,能够自动预测反映可编辑区域的编辑掩膜,并仅对掩膜区域内的笔画进行改动。 +
    +
    + International Conference on Multimedia & Expo (ICME, 2024)  (CCF-B) +
    + [论文] + [代码] +

    +
    + + +

    Video-Driven Sketch Animation via Cyclic Reconstruction Mechanism

    +

    + Zhuo Xie, Haoran Mo and Chengying Gao* +
    +
    + 简介: + 针对二维草图动画制作需要耗费大量时间绘制关键帧的问题,本文提出了一种将视频作为参考信息,实现草图图像动画生成的自动化解决方案。该方案包括从驱动视频中提取动作、在保持源草图外观特征的前提下将视频动作注入静态草图中,以产生动态的草图序列。为减少复杂运动导致的模糊伪影并保持笔划线条的连贯性,本文将草图的内部掩码图作为显式的引导信息,用于指示运动主体的内部区域并确保各运动部位的完整性。此外,为了在建立动作模型时弥合驱动帧和草图之间的域差距,本文引入一种循环重建机制,以增加动作模型对不同数据域的兼容能力,从而提高草图动画和驱动视频之间的动作一致性。定量与定性实验结果,充分验证了本文方法相比于现有图像动画方法的优越性。 +
    +
    + International Conference on Multimedia & Expo (ICME, 2024)  (CCF-B) +
    +

    +
    @@ -214,7 +257,7 @@

    General Virtual Ske 这种以绘画方式生成矢量线稿草图的方法基于本工作提出的动态窗口机制, 即模拟一根虚拟的笔在输入图像上不断移动并同时根据笔触周围的窗口区域情况进行绘制,直至绘制完成。 模型使用了一个可微渲染模块,能够允许仅利用像素图像进行训练学习,而无需依赖矢量数据进行直接监督。 - 另外,此工作提出了一个笔划正则化的机制去引导模型绘制更少但更长的线条,来使生成的矢量线稿草图更简洁。 + 另外,此工作提出了一个笔画正则化的机制去引导模型绘制更少但更长的线条,来使生成的矢量线稿草图更简洁。 大量实验证明本模型能生成高质量的矢量线稿草图,同时具有运行时间更短、泛化能力更强的优点。

    @@ -641,10 +684,12 @@

    Automat
    -

    三维渲染与建模

    +

    多媒体处理与三维渲染建模

    - 实验室关注基于窄带的快速流体表面重建和织物建模与渲染等三维渲染与建模研究方向。 + 在多媒体处理处理方面,实验室关注音乐和舞蹈等内容的生成和理解。 +
    + 在三维渲染与建模方面,实验室关注基于窄带的快速流体表面重建和织物建模与渲染等研究方向:

     

    基于窄带的快速流体表面重建:流体表面网格的重建介于流体模拟和流体渲染之间, 重建出来的流体网格质量与最终渲染效果有着极其密切的关系。 @@ -660,6 +705,25 @@

    三维渲染与建模


    + + + +
    + + +

    PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training

    +

    + Xiao Liang, Zijian Zhao, Weichao Zeng, Yutong He, Fupeng He, Yiyi Wang and Chengying Gao +
    +
    + 简介: + 学习音乐结构和作曲模式对于音乐生成和理解都是必要的,但是现有方法不能统一利用所学特征来同时生成和理解音乐。在本文中,我们提出了PianoBART,这是一种基于BART进行符号化钢琴音乐生成和理解的预训练模型。我们针对PianoBART的预训练任务设计了一个多级对象选择策略,可以防止信息泄露或丢失,提高学习能力。预训练中捕获的音乐语义可针对不同音乐生成和音乐理解任务进行微调。实验表明,PianoBART 能高效地学习音乐模式,在生成高质量的连贯乐曲和理解音乐方面表现出色。 +
    +
    + International Conference on Multimedia & Expo (ICME, 2024)  (CCF-B) +
    +

    +
    diff --git a/publications_papers.html b/publications_papers.html index fac886ee..83fe547c 100644 --- a/publications_papers.html +++ b/publications_papers.html @@ -57,6 +57,9 @@

    2024

      +
    1. Xiao Liang, Zijian Zhao, Weichao Zeng, Yutong He, Fupeng He, Yiyi Wang and Chengying Gao, PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training, Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2024)  (CCF-B)
    2. +
    3. Zhuo Xie, Haoran Mo and Chengying Gao*, Video-Driven Sketch Animation via Cyclic Reconstruction Mechanism, Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2024)  (CCF-B)
    4. +
    5. Haoran Mo, Xusheng Lin, Chengying Gao* and Ruomei Wang, Text-based Vector Sketch Editing with Image Editing Diffusion Prior, Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2024)  (CCF-B)
    6. Haoran Mo, Chengying Gao* and Ruomei Wang, Joint Stroke Tracing and Correspondence for 2D Animation, ACM Transactions on Graphics (Presented at SIGGRAPH 2024)  (CCF-A)