Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence
  with Egocentric-Exocentric Vision
Perceiving the world from both egocentric (first-person) and exocentric
(third-person) perspectives is fundamental to human cognition, enabling rich
and complementary understanding of dynamic environments. In recent years,
allowing the machines to leverage the synergistic potential of these dual
perspectives has emerged as a compelling research direction in video
understanding. In this survey, we provide a comprehensive review of video
understanding from both exocentric and egocentric viewpoints. We begin by
highlighting the practical applications of integrating egocentric and
exocentric techniques, envisioning their potential collaboration across
domains. We then identify key research tasks to realize these applications.
Next, we systematically organize and review recent advancements into three main
research directions: (1) leveraging egocentric data to enhance exocentric
understanding, (2) utilizing exocentric data to improve egocentric analysis,
and (3) joint learning frameworks that unify both perspectives. For each
direction, we analyze a diverse set of tasks and relevant works. Additionally,
we discuss benchmark datasets that support research in both perspectives,
evaluating their scope, diversity, and applicability. Finally, we discuss
limitations in current works and propose promising future research directions.
By synthesizing insights from both perspectives, our goal is to inspire
advancements in video understanding and artificial intelligence, bringing
machines closer to perceiving the world in a human-like manner. A GitHub repo
of related works can be found at
https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision.
68469b773ec10bdd8ab4db88
Yuping He
68469b773ec10bdd8ab4db89
Yifei Huang
68469b773ec10bdd8ab4db8a
Guo Chen
claimed_verified
2025-06-09T10:11:02.757Z
6392c73390b8e99a6779a7b0
/avatars/9ff824ab02848120aec5e8de6780bcf1.svg
Guo Chen
user
cg1177
68469b773ec10bdd8ab4db8b
Lidong Lu
claimed_verified
2025-06-11T08:36:43.720Z
64a3de701698ad2985277148
/avatars/09eebadbbea53ed2800591564ff5c931.svg
lulidong
user
lulidong
68469b773ec10bdd8ab4db8c
Baoqi Pei
68469b773ec10bdd8ab4db8d
Jilan Xu
68469b773ec10bdd8ab4db8e
Tong Lu
68469b773ec10bdd8ab4db8f
Yoichi Sato
Bridging Perspectives: A Survey on Cross-view Collaborative Intelligence
  with Egocentric-Exocentric Vision
Perceiving the world from both egocentric (first-person) and exocentric
(third-person) perspectives is fundamental to human cognition, enabling rich
and complementary understanding of dynamic environments. In recent years,
allowing the machines to leverage the synergistic potential of these dual
perspectives has emerged as a compelling research direction in video
understanding. In this survey, we provide a comprehensive review of video
understanding from both exocentric and egocentric viewpoints. We begin by
highlighting the practical applications of integrating egocentric and
exocentric techniques, envisioning their potential collaboration across
domains. We then identify key research tasks to realize these applications.
Next, we systematically organize and review recent advancements into three main
research directions: (1) leveraging egocentric data to enhance exocentric
understanding, (2) utilizing exocentric data to improve egocentric analysis,
and (3) joint learning frameworks that unify both perspectives. For each
direction, we analyze a diverse set of tasks and relevant works. Additionally,
we discuss benchmark datasets that support research in both perspectives,
evaluating their scope, diversity, and applicability. Finally, we discuss
limitations in current works and propose promising future research directions.
By synthesizing insights from both perspectives, our goal is to inspire
advancements in video understanding and artificial intelligence, bringing
machines closer to perceiving the world in a human-like manner. A GitHub repo
of related works can be found at
https://github.com/ayiyayi/Awesome-Egocentric-and-Exocentric-Vision.
A survey on leveraging both egocentric and exocentric video understanding for enhancing complementary tasks with a focus on three research directions and benchmark datasets.
egocentric exocentric video understanding research tasks benchmark datasets