Zobrazeno 1 - 10
of 1 320
pro vyhledávání: '"Kim, Tae Kyun"'
Autor:
Kim, Sohwi, Kim, Tae-Kyun
Super-resolution methods are increasingly being specialized for both real-world and face-specific tasks. However, many existing approaches rely on simplistic degradation models, which limits their ability to handle complex and unknown degradation pat
Externí odkaz:
http://arxiv.org/abs/2410.07663
Autor:
Kim, Donghwan, Kim, Tae-Kyun
3D human shape reconstruction under severe occlusion due to human-object or human-human interaction is a challenging problem. Parametric models i.e., SMPL(-X), which are based on the statistics across human shapes, can represent whole human body shap
Externí odkaz:
http://arxiv.org/abs/2409.18364
Autor:
Vecchietti, Luiz Felipe, Lee, Minji, Hangeldiyev, Begench, Jung, Hyunkyu, Park, Hahnbeom, Kim, Tae-Kyun, Cha, Meeyoung, Kim, Ho Min
Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-
Externí odkaz:
http://arxiv.org/abs/2409.17726
Estimating the poses of both a hand and an object has become an important area of research due to the growing need for advanced vision computing. The primary challenge involves understanding and reconstructing how hands and objects interact, such as
Externí odkaz:
http://arxiv.org/abs/2409.17629
Accurate 3D object detection is crucial for autonomous vehicles and robots to navigate and interact with the environment safely and effectively. Meanwhile, the performance of 3D detector relies on the data size and annotation which is expensive. Cons
Externí odkaz:
http://arxiv.org/abs/2409.06583
Autor:
Cho, Woojin, Lee, Jihyun, Yi, Minjae, Kim, Minje, Woo, Taeyun, Kim, Donghwan, Ha, Taewook, Lee, Hyokeun, Ryu, Je-Hwan, Woo, Woontack, Kim, Tae-Kyun
Existing datasets for 3D hand-object interaction are limited either in the data cardinality, data variations in interaction scenarios, or the quality of annotations. In this work, we present a comprehensive new training dataset for hand-object intera
Externí odkaz:
http://arxiv.org/abs/2409.04033
We present InterHandGen, a novel framework that learns the generative prior of two-hand interaction. Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object. Our prior can be incorporated in
Externí odkaz:
http://arxiv.org/abs/2403.17422
Autor:
Kim, Jinseok, Kim, Tae-Kyun
Super-resolution (SR) and image generation are important tasks in computer vision and are widely adopted in real-world applications. Most existing methods, however, generate images only at fixed-scale magnification and suffer from over-smoothing and
Externí odkaz:
http://arxiv.org/abs/2403.10255
Autor:
Kim, Minje, Kim, Tae-Kyun
Creating personalized hand avatars is important to offer a realistic experience to users on AR / VR platforms. While most prior studies focused on reconstructing 3D hand shapes, some recent work has tackled the reconstruction of hand textures on top
Externí odkaz:
http://arxiv.org/abs/2403.08262
Recent endeavors have been made to leverage self-supervised depth estimation as guidance in unsupervised domain adaptation (UDA) for semantic segmentation. Prior arts, however, overlook the discrepancy between semantic and depth features, as well as
Externí odkaz:
http://arxiv.org/abs/2402.03795