2 citations · 3 across the 11 of their papers we have counts for
7 papers · 1 filter
GeoRelight: Learning Joint Geometrical Relighting and Reconstruction with Flexible Multi-Modal Diffusion Transformers
Yuxuan Xue, Ruofan Liang, Egor Zakharov +6
Relighting a person from a single photo is an attractive but ill-posed task, as a 2D image ambiguously entangles 3D geometry, intrinsic appearance, and illumination. Current method…
CamLit: Unified Video Diffusion with Explicit Camera and Lighting Control
Zhiyi Kuang, Chengan He, Egor Zakharov +6
We present CamLit, the first unified video diffusion model that jointly performs novel view synthesis (NVS) and relighting from a single input image. Given one reference image, a u…
Gaussian Pixel Codec Avatars: A Hybrid Representation for Efficient Rendering
Divam Gupta, Anuj Pahuja, Nemanja Bartolovic +3
We present Gaussian Pixel Codec Avatars (GPiCA), photorealistic head avatars that can be generated from multi-view images and efficiently rendered on mobile devices. GPiCA utilizes…
HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars
Byungjun Kim, Shunsuke Saito, Giljoo Nam +4
We present a universal prior model for 3D head avatars with explicit hair compositionality. Existing approaches to build generalizable priors for 3D head avatars often adopt a holi…
LRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
Hongbeen Park, Minjeong Park, Giljoo Nam +1
Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveragin…
LUCAS: Layered Universal Codec Avatars
Di Liu, Teng Deng, Giljoo Nam +6
Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during e…
InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion
Jihyun Lee, Shunsuke Saito, Giljoo Nam +2
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 i…