  To anticipate how a human would act in the future, it is essential to
understand the human intention since it guides the human towards a certain
goal. In this paper, we propose a hierarchical architecture which assumes a
sequence of human action (low-level) can be driven from the human intention
(high-level). Based on this, we deal with Long-Term Action Anticipation task in
egocentric videos. Our framework first extracts two level of human information
over the N observed videos human actions through a Hierarchical Multi-task MLP
Mixer (H3M). Then, we condition the uncertainty of the future through an
Intention-Conditioned Variational Auto-Encoder (I-CVAE) that generates K stable
predictions of the next Z=20 actions that the observed human might perform. By
leveraging human intention as high-level information, we claim that our model
is able to anticipate more time-consistent actions in the long-term, thus
improving the results over baseline methods in EGO4D Challenge. This work
ranked first in both CVPR@2022 and ECVV@2022 EGO4D LTA Challenge by providing
more plausible anticipated sequences, improving the anticipation of nouns and
overall actions. Webpage: https://evm7.github.io/icvae-page/
