6 papers
Skinned Motion Retargeting with Spatially Adaptive Interaction Guidance
Soojin Choi, Seokhyeon Hong, Chaelin Kim +3
Retargeting motion across characters with varying body shapes while preserving interaction semantics, such as self-contact and near-body proximity, remains a challenging problem. W…
Stylized Text-to-Motion Generation via Hypernetwork-Driven Low-Rank Adaptation
Junhyuk Jeon, Seokhyeon Hong, Junyong Noh
Text-driven motion diffusion models are capable of generating realistic human motions, but text alone often struggles to express fine-level nuances of motion, commonly referred to…
X-AVDT: Audio-Visual Cross-Attention for Robust Deepfake Detection
Youngseo Kim, Kwan Yun, Seokhyeon Hong +3
The surge of highly realistic synthetic videos produced by contemporary generative systems has significantly increased the risk of malicious use, challenging both humans and existi…
SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing
Seokhyeon Hong, Chaelin Kim, Serin Yoon +3
Text-driven motion generation has advanced significantly with the rise of denoising diffusion models. However, previous methods often oversimplify representations for the skeletal…
ASMR: Adaptive Skeleton-Mesh Rigging and Skinning via 2D Generative Prior
Seokhyeon Hong, Soojin Choi, Chaelin Kim +2
Despite the growing accessibility of skeletal motion data, integrating it for animating character meshes remains challenging due to diverse configurations of both skeletons and mes…
AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models
Kwan Yun, Seokhyeon Hong, Chaelin Kim +1
Despite recent advancements in learning-based motion in-betweening, a key limitation has been overlooked: the requirement for character-specific datasets. In this work, we introduc…