Monocular Video
Monocular video analysis focuses on reconstructing 3D scenes and objects, including humans, from single-camera video footage, aiming to overcome the inherent ambiguities of depth perception. Current research heavily utilizes neural radiance fields (NeRFs) and Gaussian splatting, often incorporating kinematic models and physics-based constraints to improve accuracy and realism, particularly for dynamic scenes and human motion capture. These advancements have significant implications for fields like virtual reality, animation, and robotics, enabling more efficient and realistic 3D content creation and scene understanding.
Papers
MOSS: Motion-based 3D Clothed Human Synthesis from Monocular Video
Hongsheng Wang, Xiang Cai, Xi Sun, Jinhong Yue, Zhanyun Tang, Shengyu Zhang, Feng Lin, Fei Wu
S3O: A Dual-Phase Approach for Reconstructing Dynamic Shape and Skeleton of Articulated Objects from Single Monocular Video
Hao Zhang, Fang Li, Samyak Rawlekar, Narendra Ahuja
Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Monocular Videos
Isabella Liu, Hao Su, Xiaolong Wang
MultiPhys: Multi-Person Physics-aware 3D Motion Estimation
Nicolas Ugrinovic, Boxiao Pan, Georgios Pavlakos, Despoina Paschalidou, Bokui Shen, Jordi Sanchez-Riera, Francesc Moreno-Noguer, Leonidas Guibas