  We present SENC, a novel self-supervised neural cloth simulator that
addresses the challenge of cloth self-collision. This problem has remained
unresolved due to the gap in simulation setup between recent collision
detection and response approaches and self-supervised neural simulators. The
former requires collision-free initial setups, while the latter necessitates
random cloth instantiation during training. To tackle this issue, we propose a
novel loss based on Global Intersection Analysis (GIA). This loss extracts the
volume surrounded by the cloth region that forms the penetration. By
constructing an energy based on this volume, our self-supervised neural
simulator can effectively address cloth self-collisions. Moreover, we develop a
self-collision-aware graph neural network capable of learning to handle
self-collisions, even for parts that are topologically distant from one
another. Additionally, we introduce an effective external force scheme that
enables the simulation to learn the cloth's behavior in response to random
external forces. We validate the efficacy of SENC through extensive
quantitative and qualitative experiments, demonstrating that it effectively
reduces cloth self-collision while maintaining high-quality animation results.
