Monocular Human

Monocular human modeling aims to reconstruct realistic 3D human avatars from single-view video or image data, a challenging problem due to inherent ambiguities in 2D-to-3D projection. Current research focuses on developing efficient and accurate neural network architectures, including those based on neural radiance fields, Gaussian splatting, and implicit representations like Fourier Occupancy Fields, to capture both rigid skeletal motion and complex non-rigid deformations like clothing. These advancements improve the speed and quality of avatar generation, enabling real-time applications and pushing the boundaries of realistic digital human representation. The resulting models have significant implications for fields like animation, virtual reality, and computer vision, offering more realistic and computationally efficient human character creation.