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Soo Ye Kim

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

I-INR: Iterative Implicit Neural Representations

Ali Haider
Muhammad Salman Ali
Maryam Qamar
Tahir Khalil
Soo Ye Kim
Jihyong Oh
Enzo Tartaglione
Sung-Ho Bae

Implicit Neural Representations (INRs) have revolutionized signal processing and computer vision by modeling signals as continuous, differentiable functions parameterized by neural networks. However, INRs are prone to the spectral bias problem, limiting their ability to retain high-frequency information, and often struggle with noise robustness. Motivated by recent trends in iterative refinement processes, we propose Iterative Implicit Neural Representations (I-INRs). This novel plug-and-play framework iteratively refines signal reconstructions to restore high-frequency details, improve noise robustness, and enhance generalization, ultimately delivering superior reconstruction quality. I-INRs integrate seamlessly into existing INR architectures with only a 0.5–2% increase in parameters. During reconstruction, the iterative refinement adds just 0.8–1.6% additional FLOPs over the baseline while delivering a substantial performance boost of up to +2.0 PSNR. Extensive experiments demonstrate that I-INRs consistently outperform WIRE, SIREN, and Gauss across various computer vision tasks, including image fitting, image denoising, and object occupancy prediction.

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NeurIPS Conference 2025 Conference Paper

OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions

Yuanhao Cai
He Zhang
Xi Chen
Jinbo Xing
Yiwei Hu
Yuqian Zhou
Kai Zhang
Zhifei Zhang

Existing feedforward subject-driven video customization methods mainly study single-subject scenarios due to the difficulty of constructing multi-subject training data pairs. Another challenging problem that how to use the signals such as depth, mask, camera, and text prompts to control and edit the subject in the customized video is still less explored. In this paper, we first propose a data construction pipeline, VideoCus-Factory, to produce training data pairs for multi-subject customization from raw videos without labels and control signals such as depth-to-video and mask-to-video pairs. Based on our constructed data, we develop an Image-Video Transfer Mixed (IVTM) training with image editing data to enable instructive editing for the subject in the customized video. Then we propose a diffusion Transformer framework, OmniVCus, with two embedding mechanisms, Lottery Embedding (LE) and Temporally Aligned Embedding (TAE). LE enables inference with more subjects by using the training subjects to activate more frame embeddings. TAE encourages the generation process to extract guidance from temporally aligned control signals by assigning the same frame embeddings to the control and noise tokens. Experiments demonstrate that our method significantly surpasses state-of-the-art methods in both quantitative and qualitative evaluations. Project page is at https: //caiyuanhao1998. github. io/project/OmniVCus/

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ICLR Conference 2025 Conference Paper

Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment

Yizhi Song
Liu He
Zhifei Zhang
Soo Ye Kim
He Zhang 0004
Wei Xiong 0008
Zhe Lin 0001
Brian L. Price

Personalized image generation has emerged from the recent advancements in generative models. However, these generated personalized images often suffer from localized artifacts such as incorrect logos, reducing fidelity and fine-grained identity details of the generated results. Furthermore, there is little prior work tackling this problem. To help improve these identity details in the personalized image generation, we introduce a new task: reference-guided artifacts refinement. We present Refine-by-Align, a first-of-its-kind model that employs a diffusion-based framework to address this challenge. Our model consists of two stages: Alignment Stage and Refinement Stage, which share weights of a unified neural network model. Given a generated image, a masked artifact region, and a reference image, the alignment stage identifies and extracts the corresponding regional features in the reference, which are then used by the refinement stage to fix the artifacts. Our model-agnostic pipeline requires no test-time tuning or optimization. It automatically enhances image fidelity and reference identity in the generated image, generalizing well to existing models on various tasks including but not limited to customization, generative compositing, view synthesis, and virtual try-on. Extensive experiments and comparisons demonstrate that our pipeline greatly pushes the boundary of fine details in the image synthesis models.

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AAAI Conference 2024 Conference Paper

Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model

Junghun Cha
Ali Haider
Seoyun Yang
Hoeyeong Jin
Subin Yang
A. F. M. Shahab Uddin
Jaehyoung Kim
Soo Ye Kim

A significant volume of analog information, i.e., documents and images, have been digitized in the form of scanned copies for storing, sharing, and/or analyzing in the digital world. However, the quality of such contents is severely degraded by various distortions caused by printing, storing, and scanning processes in the physical world. Although restoring high-quality content from scanned copies has become an indispensable task for many products, it has not been systematically explored, and to the best of our knowledge, no public datasets are available. In this paper, we define this problem as Descanning and introduce a new high-quality and large-scale dataset named DESCAN-18K. It contains 18K pairs of original and scanned images collected in the wild containing multiple complex degradations. In order to eliminate such complex degradations, we propose a new image restoration model called DescanDiffusion consisting of a color encoder that corrects the global color degradation and a conditional denoising diffusion probabilistic model (DDPM) that removes local degradations. To further improve the generalization ability of DescanDiffusion, we also design a synthetic data generation scheme by reproducing prominent degradations in scanned images. We demonstrate that our DescanDiffusion outperforms other baselines including commercial restoration products, objectively and subjectively, via comprehensive experiments and analyses.

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AAAI Conference 2020 Conference Paper

FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss

Soo Ye Kim
Jihyong Oh
Munchurl Kim

Super-resolution (SR) has been widely used to convert lowresolution legacy videos to high-resolution (HR) ones, to suit the increasing resolution of displays (e. g. UHD TVs). However, it becomes easier for humans to notice motion artifacts (e. g. motion judder) in HR videos being rendered on largersized display devices. Thus, broadcasting standards support higher frame rates for UHD (Ultra High Deﬁnition) videos (4K@60 fps, 8K@120 fps), meaning that applying SR only is insufﬁcient to produce genuine high quality videos. Hence, to up-convert legacy videos for realistic applications, not only SR but also video frame interpolation (VFI) is necessitated. In this paper, we ﬁrst propose a joint VFI-SR framework for upscaling the spatio-temporal resolution of videos from 2K 30 fps to 4K 60 fps. For this, we propose a novel training scheme with a multi-scale temporal loss that imposes temporal regularization on the input video sequence, which can be applied to any general video-related task. The proposed structure is analyzed in depth with extensive experiments.

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AAAI Conference 2020 Conference Paper

JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video

Soo Ye Kim
Jihyong Oh
Munchurl Kim

Joint learning of super-resolution (SR) and inverse tonemapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications. However, previous CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames, and are only trained with a simple L2 loss. In this paper, we take a divide-and-conquer approach in designing a novel GANbased joint SR-ITM network, called JSI-GAN, which is composed of three task-speciﬁc subnets: an image reconstruction subnet, a detail restoration (DR) subnet and a local contrast enhancement (LCE) subnet. We delicately design these subnets so that they are appropriately trained for the intended purpose, learning a pair of pixel-wise 1D separable ﬁlters via the DR subnet for detail restoration and a pixel-wise 2D local ﬁlter by the LCE subnet for contrast enhancement. Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss alongside the conventional GAN loss, which helps enhancing both local details and contrasts to reconstruct high quality HR HDR results. When all subnets are jointly trained well, the predicted HR HDR results of higher quality are obtained with at least 0. 41 dB gain in PSNR over those generated by the previous methods. The ofﬁcial Tensorﬂow code is available at https: //github. com/JihyongOh/JSI-GAN.

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