2 citations · 3 across the 12 of their papers we have counts for
12 papers
LUNA: Learning Universal 3D Human Animation Beyond Skinning
Peng Li, Rawal Khirodkar, Junxuan Li +6
Creating photorealistic, animatable 3D human avatars from monocular images still largely depends on Linear Blend Skinning (LBS) and parametric body models, which constrain expressi…
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…
Capture, Canonicalize, Splat: Zero-Shot 3D Gaussian Avatars from Unstructured Phone Images
Emanuel Garbin, Guy Adam, Oded Krams +14
We present a novel, zero-shot pipeline for creating hyperrealistic, identity-preserving 3D avatars from a few unstructured phone images. Existing methods face several challenges: s…
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…
3DGH: 3D Head Generation with Composable Hair and Face
Chengan He, Junxuan Li, Tobias Kirschstein +7
We present 3DGH, an unconditional generative model for 3D human heads with composable hair and face components. Unlike previous work that entangles the modeling of hair and face, w…
FRESA: Feedforward Reconstruction of Personalized Skinned Avatars from Few Images
Rong Wang, Fabian Prada, Ziyan Wang +10
We present a novel method for reconstructing personalized 3D human avatars with realistic animation from only a few images. Due to the large variations in body shapes, poses, and c…
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…
Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior
Chen Guo, Junxuan Li, Yash Kant +3
We present Vid2Avatar-Pro, a method to create photorealistic and animatable 3D human avatars from monocular in-the-wild videos. Building a high-quality avatar that supports animati…
Relightable Full-Body Gaussian Codec Avatars
Shaofei Wang, Tomas Simon, Igor Santesteban +15
We propose Relightable Full-Body Gaussian Codec Avatars, a new approach for modeling relightable full-body avatars with fine-grained details including face and hands. The unique ch…
URAvatar: Universal Relightable Gaussian Codec Avatars
Junxuan Li, Chen Cao, Gabriel Schwartz +5
We present a new approach to creating photorealistic and relightable head avatars from a phone scan with unknown illumination. The reconstructed avatars can be animated and relit i…
Relightable Gaussian Codec Avatars
Shunsuke Saito, Gabriel Schwartz, Tomas Simon +2
The fidelity of relighting is bounded by both geometry and appearance representations. For geometry, both mesh and volumetric approaches have difficulty modeling intricate structur…