Ego-to-exo video generation refers to generating the corresponding exocentric
video according to the egocentric video, providing valuable applications in
AR/VR and embodied AI. Benefiting from advancements in diffusion model
techniques, notable progress has been achieved in video generation. However,
existing methods build upon the spatiotemporal consistency assumptions between
adjacent frames, which cannot be satisfied in the ego-to-exo scenarios due to
drastic changes in views. To this end, this paper proposes an Intention-Driven
Ego-to-exo video generation framework (IDE) that leverages action intention
consisting of human movement and action description as view-independent
representation to guide video generation, preserving the consistency of content
and motion. Specifically, the egocentric head trajectory is first estimated
through multi-view stereo matching. Then, cross-view feature perception module
is introduced to establish correspondences between exo- and ego- views, guiding
the trajectory transformation module to infer human full-body movement from the
head trajectory. Meanwhile, we present an action description unit that maps the
action semantics into the feature space consistent with the exocentric image.
Finally, the inferred human movement and high-level action descriptions jointly
guide the generation of exocentric motion and interaction content (i.e.,
corresponding optical flow and occlusion maps) in the backward process of the
diffusion model, ultimately warping them into the corresponding exocentric
video. We conduct extensive experiments on the relevant dataset with diverse
exo-ego video pairs, and our IDE outperforms state-of-the-art models in both
subjective and objective assessments, demonstrating its efficacy in ego-to-exo
video generation.

提出了一种基于行动意图的自我转他视视频生成框架（IDE），通过利用包括人类运动和动作描述的行动意图作为视角无关表示指导视频生成，保留了内容和动作的一致性。