8 papers
GeomHair: Reconstruction of Hair Strands from Colorless 3D Scans
Rachmadio Noval Lazuardi, Artem Sevastopolsky, Egor Zakharov +2
We propose a novel method that reconstructs hair strands directly from colorless 3D scans by leveraging multi-modal hair orientation extraction. Hair strand reconstruction is a fun…
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…
GenLCA: 3D Diffusion for Full-Body Avatars from In-the-Wild Videos
Yiqian Wu, Rawal Khirodkar, Egor Zakharov +6
We present GenLCA, a diffusion-based generative model for generating and editing photorealistic full-body avatars from text and image inputs. The generated avatars are faithful to…
Large-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining
Junxuan Li, Rawal Khirodkar, Chengan He +37
High-quality 3D avatar modeling faces a critical trade-off between fidelity and generalization. On the one hand, multi-view studio data enables high-fidelity modeling of humans wit…
Realiz3D: 3D Generation Made Photorealistic via Domain-Aware Learning
Ido Sobol, Kihyuk Sohn, Yoav Blum +4
We often aim to generate images that are both photorealistic and 3D-consistent, adhering to precise geometry, material, and viewpoint controls. Typically, this is achieved by fine-…
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…