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We introduce ViLPAct, a novel vision-language benchmark for human activityplanning. It is designed for a task where embodied AI agents can reason andforecast future actions of humans based on video clips about their initialactivities and intents in text. The dataset consists of 2.9k videos from\charades extended with intents via crowdsourcing, a multi-choice question testset, and four strong baselines. One of the baselines implements a neurosymbolicapproach based on a multi-modal knowledge base (MKB), while the other ones aredeep generative models adapted from recent state-of-the-art (SOTA) methods.According to our extensive experiments, the key challenges are compositionalgeneralization and effective use of information from both modalities.

ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities