EventHPE: Event-based 3D Human Pose and Shape Estimation

@article{Zou2021EventHPEE3,
  title={EventHPE: Event-based 3D Human Pose and Shape Estimation},
  author={Shihao Zou and Chuan Guo and Xinxin Zuo and Sen Wang and Pengyu Wang and Xiaoqin Hu and Shoushun Chen and Minglun Gong and Li Cheng},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={10976-10985}
}
  • Shihao ZouChuan Guo Li Cheng
  • Published 15 August 2021
  • Computer Science
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Event camera is an emerging imaging sensor for capturing dynamics of moving objects as events, which motivates our work in estimating 3D human pose and shape from the event signals. Events, on the other hand, have their unique challenges: rather than capturing static body postures, the event signals are best at capturing local motions. This leads us to propose a two-stage deep learning approach, called EventHPE. The first-stage, FlowNet, is trained by unsupervised learning to infer optical flow… 
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Efficient Human Pose Estimation via 3D Event Point Cloud

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Event-driven Video Deblurring via Spatio-Temporal Relation-Aware Network

A new Spatio-Temporal Relation-Attention network (STRA), which model the brightness changes as an extra prior to aware blurring contexts in each frame to recover spatial texture from events constantly and develops a temporal memory block to restore long-range dependencies of event sequences continuously.

S2N: Suppression-Strengthen Network for Event-Based Recognition Under Variant Illuminations

A novel suppression-strengthen network (S2N) is presented to augment the event feature representation after suppressing the influence of degradation to generate robust event representation by adaptively perceiving the local variations between the center and surrounding regions.

A Progressive Quadric Graph Convolutional Network for 3D Human Mesh Recovery

A Progressive Quadric Graph Convolutional Network (PQ-GCN) is proposed, and a simple and fast method for 3D human mesh recovery from a single image in the wild is designed, using 66% fewer parameters than the existing method, Pose2Mesh.

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

This survey focuses on state-of-the-art methods for dense non-rigid 3D reconstruction of various deformable objects and composite scenes from monocular videos or sets of monocular views.

EventNeRF: Neural Radiance Fields from a Single Colour Event Camera

This paper proposes the first approach for 3D-consistent, dense and photorealistic novel view synthesis using just a single colour event stream as input and presents a neural radiance trained en-tirely in a self-supervised manner from events while pre-serving the original resolution of the colour event channels.

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