Yu-Lun (Alex) Liu | 劉育綸

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.

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.

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• Aug 2023: Honored to receive the 中華民國影像處理與圖形識別學會博士優等論文獎 (IPPR Best Ph.D. Thesis Award)!
• July 2023: Excited and honored to have been selected as a 玉山青年學者 (Yushan Young Fellow), 2023!
• July 2023: Two conference papers accepted in ICCV 2023
• May 2023: Talk at MediaTek Inc.
• April 2023: Invited Seminar at NTHU
• Mar 2023: Talk at the 5th AII Workshop
• Mar 2023: Invited Seminar at NYCU
• Mar 2023: Two conference papers accepted in CVPR 2023
• Jan 2023: Talk at Academia Sinica
• Nov 2022: Invited Seminar at NTHU

Our flexible approach generates a continuous stack with more images containing diverse EVs, significantly improving HDR reconstruction.

In contrast to prior works that disregard the underlying geometry by directly averaging feature volume across multiple views, our proposed successfully preserves the geometric structure with respect to the ground truth while effectively reducing the number of voxels in free space.

We build a pipeline to use pre-trained 3D GANs for correcting face perspective distortion in close-up portraits.

For handling large unbounded scenes, we dynamically allocate new local radiance fields trained with frames within a temporal window.

RoDynRF tackles the robustness problem of conventional SfM systems such as COLMAP and showcases high-fidelity dynamic view synthesis results on a wide variety of videos.

We theoretically formulate a novel training objective, called Denoising Likelihood Score Matching (DLSM) loss, for the classifier to match the gradients of the true log likelihood density.

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera.

In this paper, we propose 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).

In this work, we present a frame synthesis algorithm to achieve full-frame video stabilization.

In this paper, a learning-based multimodal tone-mapping method is proposed, which not only achieves excellent visual quality but also explores the style diversity.

We propose a data-driven approach, where a generative noise model is learned from real-world noise.

In contrast to existing learning-based methods, our core idea is to incorporate the domain knowledge of the LDR image formation pipeline into our model.

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequence of images captured by a moving camera.

For utilizing the views more effectively and reducing redundancy within views, we propose a view selection module that generates an attention map indicating the importance of each view and its potential for contributing to accurate depth estimation.

The cycle consistency loss can better utilize the training data to not only enhance the interpolation results, but also maintain the performance better with less training data.

This paper mainly focuses on creating a global background model of a video sequence using the depth maps together with the RGB pictures.

In this study, we propose a backward warping process to replace the forward warping process, and the artifacts (particularly the ones produced by quantization) are significantly reduced.

My research is made possible by the generous support of the following organizations.

Stolen from Jon Barron's website.
Last updated September 2023.