I'm interested in computer vision, deep learning, and neural rendering.
Much of my interest is currently focused on the efficient training framework of NeRF/3D-GS and synthetic data training leveraging diffusion models.
Currently on the hunt for an internship/full-time position! If you're interested, please don't hesitate to reach out. Thanks! 🌟
We introduce a 3D object extraction method for Gaussian Splatting that prunes irrelevant primitives using K-nearest neighbors analysis and compensates for occlusions with diffusion-based generative inpainting.
We propose surface-sphere augmentation to preserve camera-to-surface distances for consistent ray filtering and inner-sphere augmentation to randomize viewpoints, improving both diversity and consistency.
ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering Seunghyeon Seo,
Yeonjin Chang,
Jayeon Yoo,
Seungwoo Lee,
Hojun Lee,
Nojun Kwak CVPR 2025 Workshop on Computer Vision for Metaverse
We present ARC-NeRF, leveraging Area Ray casting to cover broader unseen views with a single ray and adaptive high-frequency regularization. Additionally, luminance consistency regularization uses relative luminance as 'free lunch' data to improve texture accuracy.
We leverage the pre-trained multimodal model CLIP to achieve state-of-the-art performance in video highlight detection by fine-tuning the encoder and integrating a novel saliency pooling technique.
We simplify outdoor scene relighting for NeRF by aligning with the sun, eliminating the need for environment maps and speeding up the process using a novel cubemap concept within the framework of TensoRF.
We utilize the flipped reflection rays as additional training resources for the few-shot novel view synthesis, leading to more accurate surface normal estimation.
We model the high-dimensional joint distribution of human keypoints with a mixture density model by Random Keypoint Grouping strategy and achieve competitive performance working in real-time by eliminating additional instance identification process.
We propose the end-to-end multi-object Detection with a Regularized Mixture Model (D-RMM), which is trained by minimizing the NLL with the proposed regularization term, maximum component maximization (MCM) loss, preventing duplicate predictions.
We model a ray with mixture density model, leading to efficient learning of density distribution with sparse inputs, and propose an effective auxiliary task of ray depth estimation for few-shot novel view synthesis.
We introduce a simple yet effective data augmentation method, Mix/UnMix (MUM), which unmixes feature tiles for the mixed image tiles for the SSOD framework.
Thanks for sharing the website template, Jon Barron. :)