Including: image inpainting, color restoration, color transfer and non-photorealistic rendering.

	Controllable Anime Image Editing via Probability of Attribute Tags Zhenghao Song, Haoran Mo, and Chengying Gao* Intro: Editing anime images via probabilities of attribute tags allows controlling the degree of the manipulation in an intuitive and convenient manner. Existing methods fall short in the progressive modification and preservation of unintended regions in the input image. We propose a controllable anime image editing framework based on adjusting the tag probabilities, in which a probability encoding network (PEN) is developed to encode the probabilities into features that capture continuous characteristic of the probabilities. Thus, the encoded features are able to direct the generative process of a pre-trained diffusion model and facilitate the linear manipulation. We also introduce a local editing module that automatically identifies the intended regions and constrains the edits to be applied to those regions only, which preserves the others unchanged. Comprehensive comparisons with existing methods indicate the effectiveness of our framework in both one-shot and linear editing modes. Results in additional applications further demonstrate the generalization ability of our approach. Pacific Graphics (PG, 2024)  (CCF-B) [Paper] [Code]
	CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer Linfeng Wen, Chengying Gao*, Changqing Zou Intro: Content affinity loss including feature and pixel affinity is a main problem which leads to artifacts in photorealistic and video style transfer. This paper proposes a new framework named CAP-VSTNet, which consists of a new reversible residual network and an unbiased linear transform module, for versatile style transfer. This reversible residual network can not only preserve content affinity but not introduce redundant information as traditional reversible networks, and hence facilitate better stylization. Empowered by Matting Laplacian training loss which can address the pixel affinity loss problem led by the linear transform, the proposed framework is applicable and effective on versatile style transfer. Computer Vision and Pattern Recognition (CVPR, 2023) (CCF-A) [Paper] [Code]
	Structural Prior Guided Image Inpainting for Complex Scene Shuxin Wei, Chengying Gao Intro: Existing deep-learning based image inpainting methods have reach plausible results for small corrupted regions with rich context information. However, these methods fail to generate semantically reasonable results and clear boundaries. In this paper, we disentangle inpainting for complex scene into two stages: semantic segmentation map inpainting and segmentation guided texture inpainting. We use feature correspondence matrix to find correlation between segmentation maps and known region of corrupted images and realize texture generation of corrupted region. International Conference on Multimedia & Expo (ICME, 2021) (*oral)  (CCF-B) [Paper]
	基于稀疏结构的复杂物体修复 高成英，徐仙儿，罗燕媚，王栋 计算机学报，2019
	An edge-refined vectorized deep colorization model for grayscale-to-color images Zhuo Su, Xiangguo Liang, Jiaming Guo, Chengying Gao, Xiaonan Luo Neurocomputing, 2018 [Paper]
	PencilArt: A Chromatic Penciling Style Generation Framework Chengying Gao, Mengyue Tang, Xiangguo Liang, Zhou Su, Changqing Zou Computer Graphics Forum (CGF), 2018  (CCF-B) [Paper]

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

	Feature Replacement in Gaussian Splatting for 3D Stylization Jinkeng Zhu, Wensheng Li, ChengYing Gao* Intro 3D generation and editing, as creative tools, have attracted increasing attention, with 3D stylization being a significant area of scene editing. However, existing 3D stylization methods suffer from several limitations, including the need for retraining for new style inputs, inadequate separation of scene content and style information, and mismatches between the stylized results and the reference style images. In this paper, we introduce a feature replacement module that utilizes reversible network to decouple content and style features, ensuring the effective substitution of style information while preserving scene content. Additionally, we propose a Feature Chamfer Loss to align the high-dimensional feature space of the generated image with the reference style image, improving consistency and visual coherence. Experimental results demonstrate that our method outperforms existing techniques in terms of generation quality and multi-view consistency, advancing the state of 3D scene stylization. Computer Graphics International (CGI, 2025)  (CCF-C) [Paper]
	Efficient Integration of Neural Representations for Dynamic Humans Wensheng Li, Lingzhe Zeng, Chengying Gao, Ning Liu* Intro: While numerous studies have explored NeRF-based novel view synthesis for dynamic humans, they often require training that exceeds several hours. In this work, we introduce an innovative approach for efficiently learning and integrating neural human representations. Specifically, we initially propose decomposing high-dimensional multi-space feature volume into several feature planes, subsequently utilizing matrix multiplication to explicitly establish the correlations between different planes. This enables the simultaneous optimization of their counterparts across all dimensions by optimizing interpolated features, efficiently integrating associated details, and accelerating the rate of convergence. Additionally, we use the proposed collaborative refinement process to iteratively enhance the canonical representation. By integrating multi-space representations, we further facilitate the co-optimization of multiple frames' time-dependent observations. Experiments demonstrate that our method can achieve high-quality free-viewpoint renderings within nearly 5 minutes of optimization. IEEE Transactions on Visualization and Computer Graphics (TVCG, 2024)  (中科院 1区/CCF-A) [Paper]
	DanceComposer: Dance-to-Music Generation Using a Progressive Conditional Music Generator Xiao Liang, Wensheng Li, Lifeng Huang and Chengying Gao Intro: A wonderful piece of music is the essence and soul of dance, which motivates the study of automatic music generation for dance. To create appropriate music from dance, cross-modal correlations between dance and music such as rhythm and style, should be considered. However, existing dance-to-music methods have difficulties in achieving rhythmic alignment and stylistic matching simultaneously. Additionally, the diversity of generated samples is limited due to the lack of available paired data. To address these issues, we propose DanceComposer, a novel dance-to-music framework, which generates rhythmically and stylistically consistent multi-track music from dance videos. DanceComposer features a Progressive Conditional Music Generator (PCMG) that gradually incorporates rhythm and style constraints, enabling both rhythmic alignment and stylistic matching. To enhance style control, we introduce a Shared Style Module (SSM) that learns cross-modal features as stylistic constraints. This allows the PCMG can be trained on extensive music-only data and diversifies generated pieces. IEEE Transactions on Multimedia (TMM, 2024)  (中科院 1区/CCF-B) [Paper]
	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 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)
	A Completely Parallel Surface Reconstruction Method for Particle-Based Fluids Wencong Yang, Chengying Gao Intro: In this paper, a fast, simple and extremely accurate narrow-band method of fluid surface is proposed firstly, which makes the surface reconstruction algorithm (such as marching cube) accurately process the valid fluid surface area, which greatly avoids the useless calculation process. At the same time, we analyze the potential race conditions and conditional branching in the reconstruction process, by using mutual exclusive prefix sum algorithm, the whole process of fluid surface reconstruction is completely parallelized, which greatly speeds up the efficiency of surface reconstruction. Computer Graphics International (CGI, 2020)  (CCF-C) [Paper]
	Fully automatic algorithm on yarn model generation Zekun Zhang [Introduction (PPT)]
	Microscopic model based real time algorithm on fabric rendering Xingrong Luo [Introduction (PPT)]

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