FreeMan: Towards Benchmarking 3D Human Pose Estimation under Real-World Conditions
arxiv(2023)
摘要
Estimating the 3D structure of the human body from natural scenes is a
fundamental aspect of visual perception. 3D human pose estimation is a vital
step in advancing fields like AIGC and human-robot interaction, serving as a
crucial technique for understanding and interacting with human actions in
real-world settings. However, the current datasets, often collected under
single laboratory conditions using complex motion capture equipment and
unvarying backgrounds, are insufficient. The absence of datasets on variable
conditions is stalling the progress of this crucial task. To facilitate the
development of 3D pose estimation, we present FreeMan, the first large-scale,
multi-view dataset collected under the real-world conditions. FreeMan was
captured by synchronizing 8 smartphones across diverse scenarios. It comprises
11M frames from 8000 sequences, viewed from different perspectives. These
sequences cover 40 subjects across 10 different scenarios, each with varying
lighting conditions. We have also established an semi-automated pipeline
containing error detection to reduce the workload of manual check and ensure
precise annotation. We provide comprehensive evaluation baselines for a range
of tasks, underlining the significant challenges posed by FreeMan. Further
evaluations of standard indoor/outdoor human sensing datasets reveal that
FreeMan offers robust representation transferability in real and complex
scenes. Code and data are available at https://wangjiongw.github.io/freeman.
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