Structure-Aware Human-Action Generation

Generating long-range skeleton-based human actions has been a challenging problem since small deviations of one frame can cause a malformed action sequence. Most existing methods borrow ideas from video generation, which naively treat skeleton nodes/joints as pixels of images without considering the rich inter-frame and intra-frame structure information, leading to potential distorted actions... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Human action generation Human3.6M SA-GCN MMD 0.134 # 1
Human action generation NTU RGB+D SA-GCN MMD 0.285 # 1

Methods used in the Paper