Title: Movement-to-Action Transformer Networks for Temporal Action Proposal Generation. Abstract: The task of generating temporal action proposals is aimed at identifying temporal intervals containing human actions in untrimmed videos. For arbitrary actions, this requires learning long-range interactions. We propose an end-to-end Movement-and-Action Transformer Network (MatNet) that uses results of human movement studies to encode actions ranging from localized, atomic, body part movements, to longer-range, semantic ones, involving movements of subsets of body parts. In particular, we make direct use of the results of Laban Movement Analysis (LMA). We use LMA-based measures of movements as computational definitions of actions. We input RGB + Flow (I3D) features and 3D pose, compute LMA based low-to-high-level movement features from it, and learn the action proposals by applying two heads on the boundary Transformer and three heads on the proposal Transformer, and using five losses with different weights. We visualize and explain relations between the movement descriptors and attention map of the action proposals. We report results from extensive experiments on the Thumos14, ActivityNet and PKU-MMD datasets, showing that MatNet achieves SOTA or better performance on the temporal action proposal generation task.