DSI-Bench: A Benchmark for Dynamic Spatial Intelligence
Reasoning about dynamic spatial relationships is essential, as both observers
and objects often move simultaneously. Although vision-language models (VLMs)
and visual expertise models excel in 2D tasks and static scenarios, their
ability to fully understand dynamic 3D scenarios remains limited. We introduce
Dynamic Spatial Intelligence and propose DSI-Bench, a benchmark with nearly
1,000 dynamic videos and over 1,700 manually annotated questions covering nine
decoupled motion patterns of observers and objects. Spatially and temporally
symmetric designs reduce biases and enable systematic evaluation of models'
reasoning about self-motion and object motion. Our evaluation of 14 VLMs and
expert models reveals key limitations: models often conflate observer and
object motion, exhibit semantic biases, and fail to accurately infer relative
relationships in dynamic scenarios. Our DSI-Bench provides valuable findings
and insights about the future development of general and expertise models with
dynamic spatial intelligence.
68f854917669bcaeecce0d81
Ziang Zhang
claimed_verified
2025-10-22T15:43:30.029Z
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/avatars/378f011d9ca08839fe80ffa259620c67.svg
ZiangZhang
user
Viglong
68f854917669bcaeecce0d82
Zehan Wang
68f854917669bcaeecce0d83
Guanghao Zhang
68f854917669bcaeecce0d84
Weilong Dai
68f854917669bcaeecce0d85
Yan Xia
68f854917669bcaeecce0d86
Ziang Yan
68f854917669bcaeecce0d87
Minjie Hong
68f854917669bcaeecce0d88
Zhou Zhao
DSI-Bench: A Benchmark for Dynamic Spatial Intelligence
Reasoning about dynamic spatial relationships is essential, as both observers
and objects often move simultaneously. Although vision-language models (VLMs)
and visual expertise models excel in 2D tasks and static scenarios, their
ability to fully understand dynamic 3D scenarios remains limited. We introduce
Dynamic Spatial Intelligence and propose DSI-Bench, a benchmark with nearly
1,000 dynamic videos and over 1,700 manually annotated questions covering nine
decoupled motion patterns of observers and objects. Spatially and temporally
symmetric designs reduce biases and enable systematic evaluation of models'
reasoning about self-motion and object motion. Our evaluation of 14 VLMs and
expert models reveals key limitations: models often conflate observer and
object motion, exhibit semantic biases, and fail to accurately infer relative
relationships in dynamic scenarios. Our DSI-Bench provides valuable findings
and insights about the future development of general and expertise models with
dynamic spatial intelligence.
DSI-Bench evaluates the dynamic spatial reasoning capabilities of vision-language and visual expertise models through a benchmark of dynamic videos and annotated questions, highlighting their limitations in understanding self-motion, object motion, and relative relationships.
vision-language models visual expertise models dynamic spatial intelligence DSI-Bench dynamic videos manually annotated questions motion patterns spatially symmetric designs temporally symmetric designs self-motion object motion semantic biases relative relationships