A Survey of Interactive Generative Video
Interactive Generative Video (IGV) has emerged as a crucial technology in
response to the growing demand for high-quality, interactive video content
across various domains. In this paper, we define IGV as a technology that
combines generative capabilities to produce diverse high-quality video content
with interactive features that enable user engagement through control signals
and responsive feedback. We survey the current landscape of IGV applications,
focusing on three major domains: 1) gaming, where IGV enables infinite
exploration in virtual worlds; 2) embodied AI, where IGV serves as a
physics-aware environment synthesizer for training agents in multimodal
interaction with dynamically evolving scenes; and 3) autonomous driving, where
IGV provides closed-loop simulation capabilities for safety-critical testing
and validation. To guide future development, we propose a comprehensive
framework that decomposes an ideal IGV system into five essential modules:
Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we
systematically analyze the technical challenges and future directions in
realizing each component for an ideal IGV system, such as achieving real-time
generation, enabling open-domain control, maintaining long-term coherence,
simulating accurate physics, and integrating causal reasoning. We believe that
this systematic analysis will facilitate future research and development in the
field of IGV, ultimately advancing the technology toward more sophisticated and
practical applications.
681441e64d6a681c7c840b1f
Jiwen Yu
admin_assigned
2025-05-05T07:43:35.508Z
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Jiwen Yu
user
VictorYuki
681441e64d6a681c7c840b20
Yiran Qin
681441e64d6a681c7c840b21
Haoxuan Che
admin_assigned
2025-05-05T07:43:15.002Z
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Haoxuan Che
user
chehx
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Quande Liu
681441e64d6a681c7c840b23
Xintao Wang
admin_assigned
2025-05-05T07:42:59.735Z
60e272ca6c78a8c122b12127
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Xintao Wang
user
Xintao
681441e64d6a681c7c840b24
Pengfei Wan
claimed_verified
2025-05-04T10:05:13.156Z
662f93942510ef5735d7ad00
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magicwpf
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magicwpf
681441e64d6a681c7c840b25
Di Zhang
admin_assigned
2025-05-05T07:42:52.723Z
644c8324f02250233d0d67d9
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Di Zhang
user
dizhang
681441e64d6a681c7c840b26
Kun Gai
681441e64d6a681c7c840b27
Hao Chen
681441e64d6a681c7c840b28
Xihui Liu
admin_assigned
2025-05-05T07:43:44.271Z
65d5ec74cd05bc1eaa125040
/avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
Xihui Liu
user
XihuiLiu
A Survey of Interactive Generative Video
Interactive Generative Video (IGV) has emerged as a crucial technology in
response to the growing demand for high-quality, interactive video content
across various domains. In this paper, we define IGV as a technology that
combines generative capabilities to produce diverse high-quality video content
with interactive features that enable user engagement through control signals
and responsive feedback. We survey the current landscape of IGV applications,
focusing on three major domains: 1) gaming, where IGV enables infinite
exploration in virtual worlds; 2) embodied AI, where IGV serves as a
physics-aware environment synthesizer for training agents in multimodal
interaction with dynamically evolving scenes; and 3) autonomous driving, where
IGV provides closed-loop simulation capabilities for safety-critical testing
and validation. To guide future development, we propose a comprehensive
framework that decomposes an ideal IGV system into five essential modules:
Generation, Control, Memory, Dynamics, and Intelligence. Furthermore, we
systematically analyze the technical challenges and future directions in
realizing each component for an ideal IGV system, such as achieving real-time
generation, enabling open-domain control, maintaining long-term coherence,
simulating accurate physics, and integrating causal reasoning. We believe that
this systematic analysis will facilitate future research and development in the
field of IGV, ultimately advancing the technology toward more sophisticated and
practical applications.
Interactive Generative Video (IGV) combines generative capabilities with interactive features to enable diverse applications across gaming, embodied AI, and autonomous driving, focusing on challenges such as real-time generation, control, memory, dynamics, and intelligence.
Interactive Generative Video (IGV) generative capabilities interactive features user engagement control signals responsive feedback virtual worlds embodied AI physics-aware environment synthesizer multimodal interaction dynamically evolving scenes autonomous driving closed-loop simulation real-time generation open-domain control long-term coherence accurate physics causal reasoning