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Found 4 papers, 1 papers with code
H4D: Human 4D Modeling by Learning Neural Compositional Representation
A simple yet effective linear motion model is proposed to provide a rough and regularized motion estimation, followed by per-frame compensation for pose and geometry details with the residual encoded in the auxiliary code.
Deep Hybrid Self-Prior for Full 3D Mesh Generation
We present a deep learning pipeline that leverages network self-prior to recover a full 3D model consisting of both a triangular mesh and a texture map from the colored 3D point cloud.
Learning Compositional Representation for 4D Captures with Neural ODE
To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp.
DeepSFM: Structure From Motion Via Deep Bundle Adjustment
The explicit constraints on both depth (structure) and pose (motion), when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology.
