  Humans constantly interact with objects in daily life tasks. Capturing such
processes and subsequently conducting visual inferences from a fixed viewpoint
suffers from occlusions, shape and texture ambiguities, motions, etc. To
mitigate the problem, it is essential to build a training dataset that captures
free-viewpoint interactions. We construct a dense multi-view dome to acquire a
complex human object interaction dataset, named HODome, that consists of
$\sim$75M frames on 10 subjects interacting with 23 objects. To process the
HODome dataset, we develop NeuralDome, a layer-wise neural processing pipeline
tailored for multi-view video inputs to conduct accurate tracking, geometry
reconstruction and free-view rendering, for both human subjects and objects.
Extensive experiments on the HODome dataset demonstrate the effectiveness of
NeuralDome on a variety of inference, modeling, and rendering tasks. Both the
dataset and the NeuralDome tools will be disseminated to the community for
further development.
