Jungho Lee

My primary areas of research are 3D neural rendering for real-world scenarios and human avatar generation with 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF).

I'm always open to collaborations or suggestions. Please feel free to contact me if you have any questions or suggestions. :)

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We propose continous motion-aware blur kernel on 3D gaussian splatting utilizing 3D rigid transformation and neural ordinary differential function to reconstruct accurate 3D scene from blurry images with real-time rendering speed.

CoCoGaussian models the CoC at the 3D Gaussian level, reconstructing the precise 3D scene and enabling sharp novel view synthesis from defocused images.

We propose a novel one-stage open-vocabulary semantic segmentation model, which effectively combines CLIP and SAM to leverage SAM’s powerful classagnostic segmentation capabilities while maintaining efficient inference.

We design a First Frame Filling Video Diffusion Inpainting model inspired by the capabilities of pre-trained image-to-video diffusion models that can transform the first frame image into a highly natural video.

We propose a guided slot attention network to reinforce spatial structural information and obtain better foreground–background separation.

We propose a hierarchically decomposed graph convolution with a novel hierarchically decomposed graph, which consider the sematic correlation between the joints and the edges of the human skeleton.

We propose a novel Spatio-Temporal Curve Network (STC-Net) for skeleton-based action recognition, which consists of spatial modules with an spatio-temporal curve (STC) module and graph convolution with dilated kernels (DK-GC)

We propose the multi-scale structural graph convolutional network, the common intersection graph convolution leverages the overlapped neighbor information between neighboring vertices for a given pair of root vertices.

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Last updated December 2024.