Intention-driven Ego-to-Exo Video Generation

• URL: http://arxiv.org/abs/2403.09194v2
• Date: Sun, 17 Mar 2024 13:28:32 GMT
• Title: Intention-driven Ego-to-Exo Video Generation
• Authors: Hongchen Luo, Kai Zhu, Wei Zhai, Yang Cao,
• Abstract summary: Ego-to-exo video generation refers to generating the corresponding exo-ego video according to the egocentric model. This paper proposes an Intention-Driven-exo generation framework (IDE) that leverages action description as view-independent representation. We conduct experiments on the relevant dataset with diverse exo-ego video pairs, and ourIDE outperforms state-of-the-art models in both subjective and objective assessments.
• Score: 16.942040396018736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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