Search Results for author:
Found 6 papers, 2 papers with code
Leveraging Intrinsic Properties for Non-Rigid Garment Alignment
To achieve wrinkle-level as well as texture-level alignment, we present a novel coarse-to-fine two-stage method that leverages intrinsic manifold properties with two neural deformation fields, in the 3D space and the intrinsic space, respectively.
CaPhy: Capturing Physical Properties for Animatable Human Avatars
In contrast to previous work on 3D avatar reconstruction, our method is able to generalize to novel poses with realistic dynamic cloth deformations.
CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition
In this way, the clothing deformations are disentangled such that the pose-dependent wrinkles can be better learned and applied to unseen poses.
Learning Implicit Templates for Point-Based Clothed Human Modeling
We present FITE, a First-Implicit-Then-Explicit framework for modeling human avatars in clothing.
Learning Modified Indicator Functions for Surface Reconstruction
We design a novel deep neural network to perform surface integral and learn the modified indicator functions from un-oriented and noisy point clouds.
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments.
