Kwon Byung-Ki

Kwon Byung-Ki, Kim Sung-Bin, Tae-Hyun Oh

Recent work on dense optical flow has shown significant progress, primarily in a supervised learning manner requiring a large amount of labeled data. Due to the expensiveness of obtaining large scale real-world data, computer graphics are typically leveraged for constructing datasets. However, there is a common belief that synthetic-to-real domain gaps limit generalization to real scenes. In this paper, we show that the required characteristics in an optical flow dataset are rather simple and present a simpler synthetic data generation method that achieves a certain level of realism with compositions of elementary operations. With 2D motion-based datasets, we systematically analyze the simplest yet critical factors for generating synthetic datasets. Furthermore, we propose a novel method of utilizing occlusion masks in a supervised method and observe that suppressing gradients on occluded regions serves as a powerful initial state in the curriculum learning sense. The RAFT network initially trained on our dataset outperforms the original RAFT on the two most challenging online benchmarks, MPI Sintel and KITTI 2015.

Hyunwoo Ha, Oh Hyun-Bin, Kim Jun-Seong et al.

Video motion magnification is a technique to capture and amplify subtle motion in a video that is invisible to the naked eye. The deep learning-based prior work successfully demonstrates the modelling of the motion magnification problem with outstanding quality compared to conventional signal processing-based ones. However, it still lags behind real-time performance, which prevents it from being extended to various online applications. In this paper, we investigate an efficient deep learning-based motion magnification model that runs in real time for full-HD resolution videos. Due to the specified network design of the prior art, i.e. inhomogeneous architecture, the direct application of existing neural architecture search methods is complicated. Instead of automatic search, we carefully investigate the architecture module by module for its role and importance in the motion magnification task. Two key findings are 1) Reducing the spatial resolution of the latent motion representation in the decoder provides a good trade-off between computational efficiency and task quality, and 2) surprisingly, only a single linear layer and a single branch in the encoder are sufficient for the motion magnification task. Based on these findings, we introduce a real-time deep learning-based motion magnification model with4.2X fewer FLOPs and is 2.7X faster than the prior art while maintaining comparable quality.

Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong et al.

Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted systems that have complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve legibility since the intricate motions still retain complex characteristics even after being magnified, which may distract us from analyzing them. In this work, we focus on improving legibility by proposing a new concept, axial motion magnification, which magnifies decomposed motions along the user-specified direction. Axial motion magnification can be applied to various applications where motions of specific axes are critical, by providing simplified and easily readable motion information. To achieve this, we propose a novel Motion Separation Module that enables to disentangle and magnify the motion representation along axes of interest. Furthermore, we build a new synthetic training dataset for the axial motion magnification task. Our proposed method improves the legibility of resulting motions along certain axes by adding a new feature: user controllability. Axial motion magnification is a more generalized concept; thus, our method can be directly adapted to the generic motion magnification and achieves favorable performance against competing methods.

Wonjun Jo, Kwon Byung-Ki, Kim Ji-Yeon et al.

LiDAR is a crucial sensor in autonomous driving, commonly used alongside cameras. By exploiting this camera-LiDAR setup and recent advances in image representation learning, prior studies have shown the promising potential of image-to-LiDAR distillation. These prior arts focus on the designs of their own losses to effectively distill the pre-trained 2D image representations into a 3D model. However, the other parts of the designs have been surprisingly unexplored. We find that fundamental design elements, e.g., the LiDAR coordinate system, quantization according to the existing input interface, and data utilization, are more critical than developing loss functions, which have been overlooked in prior works. In this work, we show that simple fixes to these designs notably outperform existing methods by 16% in 3D semantic segmentation on the nuScenes dataset and 13% in 3D object detection on the KITTI dataset in downstream task performance. We focus on overlooked design choices along the spatial and temporal axes. Spatially, prior work has used cylindrical coordinate and voxel sizes without considering their side effects yielded with a commonly deployed sparse convolution layer input interface, leading to spatial quantization errors in 3D models. Temporally, existing work has avoided cumbersome data curation by discarding unsynced data, limiting the use to only the small portion of data that is temporally synced across sensors. We analyze these effects and propose simple solutions for each overlooked aspect.