  Understanding how humans would behave during hand-object interaction is vital
for applications in service robot manipulation and extended reality. To achieve
this, some recent works have been proposed to simultaneously forecast hand
trajectories and object affordances on human egocentric videos. The joint
prediction serves as a comprehensive representation of future hand-object
interactions in 2D space, indicating potential human motion and motivation.
However, the existing approaches mostly adopt the autoregressive paradigm for
unidirectional prediction, which lacks mutual constraints within the holistic
future sequence, and accumulates errors along the time axis. Meanwhile, these
works basically overlook the effect of camera egomotion on first-person view
predictions. To address these limitations, we propose a novel diffusion-based
interaction prediction method, namely Diff-IP2D, to forecast future hand
trajectories and object affordances concurrently in an iterative
non-autoregressive manner. We transform the sequential 2D images into latent
feature space and design a denoising diffusion model to predict future latent
interaction features conditioned on past ones. Motion features are further
integrated into the conditional denoising process to enable Diff-IP2D aware of
the camera wearer's dynamics for more accurate interaction prediction.
Extensive experiments demonstrate that our method significantly outperforms the
state-of-the-art baselines on both the off-the-shelf metrics and our newly
proposed evaluation protocol. This highlights the efficacy of leveraging a
generative paradigm for 2D hand-object interaction prediction. The code of
Diff-IP2D will be released at https://github.com/IRMVLab/Diff-IP2D.
