  Reconstructing 3D human-object interaction (HOI) from single-view RGB images
is challenging due to the absence of depth information and potential
occlusions. Existing methods simply predict the body poses merely rely on
network training on some indoor datasets, which cannot guarantee the
rationality of the results if some body parts are invisible due to occlusions
that appear easily. Inspired by the end-effector localization task in robotics,
we propose a kinematics-based method that can drive the joints of human body to
the human-object contact regions accurately. After an improved forward
kinematics algorithm is proposed, the Multi-Layer Perceptron is introduced into
the solution of inverse kinematics process to determine the poses of joints,
which achieves precise results than the commonly-used numerical methods in
robotics. Besides, a Contact Region Recognition Network (CRRNet) is also
proposed to robustly determine the contact regions using a single-view video.
Experimental results demonstrate that our method outperforms the
state-of-the-art on benchmark BEHAVE. Additionally, our approach shows good
portability and can be seamlessly integrated into other methods for
optimizations.
