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I am an Assistant Professor in the Department of Computer Science at National Yang Ming Chiao Tung University. I work on image/video processing, computer vision, and computational photography, particularly on essential problems requiring machine learning with insights from geometry and domain-specific knowledge. Prior to joining NYCU, I was a Research Scientist Intern at Meta Reality Labs Research and a senior software engineer at MediaTek Inc. I received my PhD from NTU, CSIE in 2022, where I was a member of CMLab. I received the 教育部玉山青年學者, 國科會 2030 新秀學者, Google Research Scholar Award, 李國鼎青年研究獎, 資訊年輕學者卓越貢獻獎, and CVPR 2024 Outstanding Reviewer award. Dear prospective students: I am looking for undergraduate / master's / Ph.D. / postdoc students to join my group. If you are interested in working with me and want to conduct research in image processing, computer vision, and machine learning, don't hesitate to contact me directly with your CV and transcripts. Email / CV / Google Scholar / Facebook / Instagram / Github / YouTube |
For those who personally know me, that might be thinking: Who is this guy? Hover over to see how I usually look like before a paper submission deadline. Meet my cats: 虎皮捲 and 雞蛋糕! |
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赵祯俊 
黃怡川 
黃正輝 
端木竣偉 
呂承祐 
蘇智海 
葉長瀚 
陳霆軒 
簡浩任 
李沅罡 
楊承諭 
林晉暘 
吳中赫 
嚴士函 
陳捷文 
許皓翔 
范丞德 
張宸瑋 
林佑庭 
黃亭幃 
陳映寰 
張欀齡 
鄭淮薰 
柯柏旭 
王皓平 
戚維凌 
俞柏帆 
黃靖恩 
張維程 
胡智堯 
李明謙 
孫揚喆 
李宗諺
楊宗儒 
劉珆睿 
蔡師睿 
李杰穎 
陳楊融
吳定霖 
司徒立中 
朱驛庭 
徐和
翁晨昱 
何義翔 
謝侑哲 
蔡聿瑋 
鄭名翔 
葉家蓁
林揚森
蔡昀錚 
邢捷 
葉孟昀 
徐顥 
李佑軒 
周廷威
陳芝瑄 
曾士珍 
楊睿軒 
陳璽安 
張盛瑋 
鄭伯俞
陳俊瑋
林奕杰
羅宇呈
郭玠甫
葉柔昀 
鄭又豪
丁祐承 
謝明翰 
施惟智 
朱劭璿 
吳秉宸
陳士弘
陳昱佑 
陳凱昕
吳俊宏 |
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Chen-Wei Chang, Cheng-De Fan, Chia-Che Chang, Yi-Chen Lo, Yu-Chee Tseng, Jiun-Long Huang, Yu-Lun Liu CVPR, 2025 
project page / arXiv / code [coming soon] GCC demonstrates superior robustness in cross-camera scenarios, achieving state-of-the-art worst-25% error rates and highlights the method's stability and generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile solution for real-world applications. |
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Chung-Ho Wu*, Yang-Jung Chen*, Ying-Huan Chen, Jie-Ying Lee, Bo-Hsu Ke, Chun-Wei Tuan Mu, Yi-Chuan Huang, Chin-Yang Lin, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu CVPR, 2025
project page / arXiv / code [coming soon] The approach introduces (1) depth-aware unseen mask generation for accurate occlusion identification, (2) Adaptive Guided Depth Diffusion, a zero-shot method for accurate initial point placement without requiring additional training, and (3) SDEdit-based detail enhancement for multi-view coherence. |
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Cheng-De Fan, Chen-Wei Chang, Yi-Ruei Liu , Jie-Ying Lee, Jiun-Long Huang, Yu-Chee Tseng, Yu-Lun Liu CVPR, 2025
project page / arXiv / results / code [coming soon] SpectroMotion is presented, a novel approach that combines 3D Gaussian Splatting with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes and is the only existing 3DGS method capable of synthesizing photorealistic real-world dynamic specular scenes. |
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Chin-Yang Lin*, Chung-Ho Wu*, Chang-Han Yeh, Shih-Han Yen, Cheng Sun, Yu-Lun Liu CVPR, 2025
project page / arXiv / code [coming soon] FrugalNeRF is introduced, a novel few-shot NeRF framework that leverages weight-sharing voxels across multiple scales to efficiently represent scene details and guides training without relying on externally learned priors, enabling full utilization of the training data. |
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Chun-Hung Wu, Shih-Hong Chen, Chih-Yao Hu, Hsin-Yu Wu, Kai-Hsin Chen, Yu-You Chen, Chih-Hai Su, Chih-Kuo Lee, Yu-Lun Liu CVPR, 2025
project page / arXiv / code & dataset [coming soon] / colab DeNVeR is presented, an unsupervised approach for vessel segmentation in X-ray videos without annotated ground truth, providing a robust, data-efficient tool for disease diagnosis and treatment planning and setting a new standard for future research in video vessel segmentation. |
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Yu-Ting Zhan*, Cheng-Yuan Ho*, Hebi Yang, Jui-Chiu Chiang, Yu-Lun Liu, Wen-Hsiao Peng ICLR, 2025
arXiv This work introduces a context-adaptive triplane approach to their rate-distortion-optimized 3DGS compression, which achieves the state-of-the-art compression performance on the commonly used real-world datasets. |
 
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Hao-Yu Hou*, Chia-Chi Hsu*, Yu-Chen Huang*, Mu-Yi Shen*, Wei-Fang Sun, Cheng Sun, Chia-Che Chang, Yu-Lun Liu, Chun-Yi Lee ICASSP, 2025
paper In this study, we propose an uncertainty estimation approach to assist 3DGS in reconstructing 3D scenes and removing transients. |
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Jui-Che Chiang, Hou-Ning Hu, Bo-Syuan Hou, Chia-Yu Tseng, Yu-Lun Liu, Min-Hung Chen, Yen-Yu Lin WACV, 2025 (Oral Presentation) project page / arXiv / code A novel transformer-based method that can detect non-visible regions and recover their missing features from visible parts and performs favorably against the state of the arts on challenging datasets such as WFLW and COFW. |
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Kuan-Hung Liu, Cheng-Kun Yang, Min-Hung Chen, Yu-Lun Liu, Yen-Yu Lin WACV, 2025 (Oral Presentation) project page / arXiv / code Experimental results demonstrate that CorrFill significantly enhances the performance of multiple baseline diffusion-based methods, including state-of-the-art approaches, by emphasizing faithfulness to the reference images. |
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Yang-Che Sun, Cheng Yu Yeo, Ernie Chu, Jun-Cheng Chen, Yu-Lun Liu arXiv, 2024 project page / arXiv / code [coming soon] This work derives a SR model, which includes a Coefficient Backbone and Basis Swin Transformer for generalizable Factorized Fields, and introduces a merged-basis compression branch that consolidates shared structures, further optimizing the compression process. |
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Chang-Han Yeh, Chin-Yang Lin, Zhixiang Wang, Chi-Wei Hsiao, Ting-Hsuan Chen, Yu-Lun Liu arXiv, 2024 project page / arXiv / code / demo It is shown that this method not only achieves top performance in zero-shot video restoration but also significantly surpasses trained models in generalization across diverse datasets and extreme degradations. |
 
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Chi-Wei Hsiao, Yu-Lun Liu, Cheng-Kun Yang, Sheng-Po Kuo, Yucheun Kevin Jou, Chia-Ping Chen NeurIPS, 2024 project page / paper / code This work proposes ReF-LDM, an adaptation of LDM designed to generate HQ face images conditioned on one LQ image and multiple HQ reference images, and designs a timestep-scaled identity loss, enabling the LDM-based model to focus on learning the discriminating features of human faces. |
 
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Ning-Hsu Wang, Yu-Lun Liu NeurIPS, 2024 project page / arXiv / code / demo This work proposes a new depth estimation framework that utilizes unlabeled 360-degree data effectively and uses state-of-the-art perspective depth estimation models as teacher models to generate pseudo labels through a six-face cube projection technique, enabling efficient labeling of depth in 360-degree images. |
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Ting-Hsuan Chen, Jiewen Chan, Hau-Shiang Shiu, Shih-Han Yen, Chang-Han Yeh, Yu-Lun Liu NeurIPS, 2024 project page / arXiv / results / code / video / demo A video editing framework, NaRCan, which integrates a hybrid deformation field and diffusion prior to generate high-quality natural canonical images to represent the input video and employs multi-layer perceptrons (MLPs) to capture local residual deformations, enhancing the model's ability to handle complex video dynamics. |
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Shih-Wei Guo, Tsu-Ching Hsiao, Yu-Lun Liu, Chun-Yi Lee IROS, 2024 project page / arXiv / video A novel coarse-to-fine continuous pose diffusion method to enhance the precision of pick-and-place operations within robotic manipulation tasks and facilitates the accurate perception of object poses, which enables more precise object manipulation. |
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Ming-Feng Li, Yueh-Feng Ku, Hong-Xuan Yen, Chi Liu, Yu-Lun Liu, Albert Y. C. Chen, Cheng-Hao Kuo, Min Sun ECCV, 2024 project page / arXiv / code The proposed GenRC, an automated training-free pipeline to complete a room-scale 3D mesh with high-fidelity textures, outperforms state-of-the-art methods under most appearance and geometric metrics on ScanNet and ARKitScenes datasets, even though GenRC is not trained on these datasets nor using predefined camera trajectories. |
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Mu-Yi Shen, Chia-Chi Hsu, Hao-Yu Hou, Yu-Chen Huang, Wei-Fang Sun, Chia-Che Chang, Yu-Lun Liu, Chun-Yi Lee ICRA RoboNerF Workshop, 2024 project page / video / arXiv / code The DriveEnv-NeRF framework, which leverages Neural Radiance Fields (NeRF) to enable the validation and faithful forecasting of the efficacy of autonomous driving agents in a targeted real-world scene, can serve as a training environment for autonomous driving agents under various lighting conditions. |
 
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Zhixiang Wang, Baiang Li, Jian Wang, Yu-Lun Liu, Jinwei Gu, Yung-Yu Chuang, Shin'ichi Satoh SIGGRAPH, 2024 project page / arXiv / code [coming soon] / slides / supplement An innovative approach for image matting that redefines the traditional regression-based task as a generative modeling challenge and harnesses the capabilities of latent diffusion models, enriched with extensive pre-trained knowledge, to regularize the matting process. |
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Chih-Hai Su*, Chih-Yao Hu*, Shr-Ruei Tsai*, Jie-Ying Lee*, Chin-Yang Lin, Yu-Lun Liu SIGGRAPH, 2024 project page / arXiv / code / video This paper presents a novel approach called BoostMVSNeRFs to enhance the rendering quality of MVS-based NeRFs in large-scale scenes, and identifies limitations in MVS-based NeRF methods, such as restricted viewport coverage and artifacts due to limited input views. |
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Ji-Jia Wu, Andy Chia-Hao Chang, Chieh-Yu Chuang, Chun-Pei Chen, Yu-Lun Liu, Min-Hung Chen, Hou-Ning Hu, Yung-Yu Chuang, Yen-Yu Lin CVPR, 2024 arXiv / code This paper addresses text-supervised semantic segmentation, aiming to learn a model capable of segmenting arbitrary visual concepts within images by using only imagetext pairs without dense annotations. |
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Caoyuan Ma, Yu-Lun Liu, Zhixiang Wang, Wu Liu, Xinchen Liu, Zheng Wang CVPR, 2024 project page / arXiv / code This work reconstructs the previous HumanNeRF approach, combining explicit and implicit human representations with both general and specific mapping processes, and shows that explicit shape can filter the information used to fit implicit representation, and frozen general mapping combined with point-specific mapping can effectively avoid overfitting and improve pose generalization performance. |
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Yu-Ju Tsai, Yu-Lun Liu, Lu Qi, Kelvin C.K. Chan, Ming-Hsuan Yang ICLR, 2024 project page / arXiv This work proposes a novel dual-branch framework named DAEFR, which introduces an auxiliary LQ branch that extracts crucial information from the LQ inputs and incorporates association training to promote effective synergy between the two branches, enhancing code prediction and output quality. |
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Zhixiang Wang, Yu-Lun Liu, Jia-Bin Huang, Shin'ichi Satoh, Sizhuo Ma, Guru Krishnan, Jian Wang IJCV, 2024 project page / arXiv This work proposes a simple yet effective method for correcting perspective distortions in a single close-up face using GAN inversion using a perspective-distorted input facial image, and develops starting from a short distance, optimization scheduling, reparametrizations, and geometric regularization. |
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Bo-Yu Cheng, Wei-Chen Chiu, Yu-Lun Liu AAAI, 2024 project page / arXiv / code An algorithm that allows joint refinement of camera pose and scene geometry represented by decomposed low-rank tensor, using only 2D images as supervision is proposed, which achieves an equivalent effect to brute-force 3D convolution with only incurring little computational overhead. |
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Su-Kai Chen, Hung-Lin Yen, Yu-Lun Liu, Min-Hung Chen, Hou-Ning Hu, Wen-Hsiao Peng, Yen-Yu Lin ICCV, 2023 project page / arXiv / code / video This work proposes the continuous exposure value representation (CEVR), which uses an implicit function to generate LDR images with arbitrary EVs, including those unseen during training, to improve HDR reconstruction. |
 
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Tao Tu, Shun-Po Chuang, Yu-Lun Liu, Cheng Sun, Ke Zhang, Donna Roy, Cheng-Hao Kuo, Min Sun ICCV, 2023 project page / arXiv The studies indicate that the proposed image-induced geometry-aware representation can enable image-based methods to attain superior detection accuracy than the seminal point cloud-based method, VoteNet, in two practical scenarios: (1) scenarios where point clouds are sparse and noisy, such as in ARKitScenes, and (2) scenarios involve diverse object classes, particularly classes of small objects, as in the case in ScanNet200. |
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Andreas Meuleman, Yu-Lun Liu, Chen Gao, Jia-Bin Huang, Changil Kim, Min H. Kim, Johannes Kopf CVPR, 2023 project page / paper / code / video This work presents an algorithm for reconstructing the radiance field of a large-scale scene from a single casually captured video, and shows that progressive optimization significantly improves the robustness of the reconstruction. |
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Yu-Lun Liu, Chen Gao, Andreas Meuleman, Hung-Yu Tseng, Ayush Saraf, Changil Kim, Yung-Yu Chuang, Johannes Kopf, Jia-Bin Huang CVPR, 2023 project page / arXiv / code / video This work addresses the robustness issue by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length) and shows favorable performance over the state-of-the-art dynamic view synthesis methods. |
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Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee ICLR, 2022 arXiv / OpenReview This work forms a novel training objective, called Denoising Likelihood Score Matching (DLSM) loss, for the classifier to match the gradients of the true log likelihood density, and concludes that the conditional scores can be accurately modeled, and the effect of the score mismatch issue is alleviated. |
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Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang TPAMI, 2021 project page / arXiv / code / demo / video This work alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network, facilitates accommodating potential errors in the flow estimation and brittle assumptions, such as brightness consistency. |
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Ning-Hsu Wang, Ren Wang, Yu-Lun Liu, Yu-Hao Huang, Yu-Lin Chang, Chia-Ping Chen, Kevin Jou ICCV, 2021 project page / arXiv / code This paper proposes a method to estimate not only a depth map but an AiF image from a set of images with different focus positions (known as a focal stack), and shows that this method outperforms the state-of-the-art methods both quantitatively and qualitatively, and also has higher efficiency in inference time. |
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Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang ICCV, 2021 project page / arXiv / poster / slides / code / demo / video / Two minute video This work presents a frame synthesis algorithm to achieve full-frame video stabilization that first estimate dense warp fields from neighboring frames and then synthesize the stabilized frame by fusing the warped contents. |
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Chien-Chuan Su, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Soo-Chang Pei ICPR, 2020 arXiv This paper proposes a learning-based multimodal tone-mapping method, which not only achieves excellent visual quality but also explores the style diversity and shows that the proposed method performs favorably against state-of-the-art tone-Mapping algorithms both quantitatively and qualitatively. |
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Ke-Chi Chang, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang, Hwann-Tzong Chen ECCV, 2020 project page / arXiv / code A data-driven approach, where a generative noise model is learned from real-world noise, which is camera-aware and quantitatively and qualitatively outperforms existing statistical noise models and learning-based methods. |
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Yu-Lun Liu*, Wei-Sheng Lai*, Yu-Sheng Chen, Yi-Lung Kao, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang CVPR, 2020 project page / arXiv / poster / slides / code / demo / 1-minute video This work model the HDR-to-LDR image formation pipeline as the dynamic range clipping, non-linear mapping from a camera response function, and quantization, and proposes to learn three specialized CNNs to reverse these steps. |
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Yu-Lun Liu, Wei-Sheng Lai, Ming-Hsuan Yang, Yung-Yu Chuang, Jia-Bin Huang CVPR, 2020 project page / arXiv / poster / slides / code / demo / 1-minute video / video / New Scientists The method leverages the motion differences between the background and the obstructing elements to recover both layers and alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network. |
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Yu-Ju Tsai*, Yu-Lun Liu*, Yung-Yu Chuang, Ming Ouhyoung AAAI, 2020 paper / code / benchmark A novel deep network for estimating depth maps from a light field image that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation and enforce symmetry in the attention map to improve accuracy. |
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Yu-Lun Liu, Yi-Tung Liao, Yen-Yu Lin, Yung-Yu Chuang AAAI, 2019 (Oral Presentation) project page / paper / poster / slides / code / video A new loss term, the cycle consistency loss, which can better utilize the training data to not only enhance the interpolation results, but also maintain the performance better with less training data is introduced. |
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Yu-Lun Liu, Hsueh-Ming Hang APSIPA, 2014 paper This paper focuses on creating a global background model of a video sequence using the depth maps together with the RGB pictures, and develops a recursive algorithm that iterates between the depth map and color pictures. |
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Du-Hsiu Li, Hsueh-Ming Hang, Yu-Lun Liu PCS, 2013 paper A backward warping process is proposed to replace the forward warped process, and the artifacts (particularly the ones produced by quantization) are significantly reduced, so the subjective quality of the synthesized virtual view images is thus much improved. |
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Area Chair, MVA 2025
Area Chair, NeurIPS 2025 Technical Papers COI Coordinator, SIGGRAPH Asia 2025 Publication Chair, ACCV 2024 Conference Reviewer: CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, SIGGRAPH, SIGGRAPH Asia, AAAI, IJCAI Journal Reviewer: TPAMI, IJCV, TIP, TOG |
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NYCU - Spring 2023, Fall 2023, Fall 2024 (Instructor) |
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NYCU - Spring 2024, Spring 2025 (Instructor) |
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NCTU - Spring 2013 (Teaching Assistant) |
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My research is made possible by the generous support of the following organizations.
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Stolen from Jon Barron's website.
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