DiffAnt: Diffusion Models for Action Anticipation
Zeyun Zhong Chengzhi Wu Manuel Martin Michael Voit Juergen Gall Jürgen Beyerer
Anticipating future actions is inherently uncertain. Given an observed video
segment containing ongoing actions, multiple subsequent actions can plausibly
follow. This uncertainty becomes even larger when predicting far into the
future. However, the majority of existing action anticipation models adhere to
a deterministic approach, neglecting to account for future uncertainties. In
this work, we rethink action anticipation from a generative view, employing
diffusion models to capture different possible future actions. In this
framework, future actions are iteratively generated from standard Gaussian
noise in the latent space, conditioned on the observed video, and subsequently
transitioned into the action space. Extensive experiments on four benchmark
datasets, i.e., Breakfast, 50Salads, EpicKitchens, and EGTEA Gaze+, are
performed and the proposed method achieves superior or comparable results to
state-of-the-art methods, showing the effectiveness of a generative approach
for action anticipation. Our code and trained models will be published on
GitHub.