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Found 6 papers, 2 papers with code
SEFD: Learning to Distill Complex Pose and Occlusion
This paper addresses the problem of three-dimensional (3D) human mesh estimation in complex poses and occluded situations.
Ranked #1 on
2D Human Pose Estimation
on OCHuman
Out of Sight, Out of Mind: A Source-View-Wise Feature Aggregation for Multi-View Image-Based Rendering
Finally, for each element in the feature set, the aggregation features are extracted by calculating the weighted means and variances, where the weights are derived from the similarity distributions.
Self-Supervised Depth Estimation with Isometric-Self-Sample-Based Learning
Most previous methods have alleviated this issue by removing the dynamic regions in the photometric loss formulation based on the masks estimated from another module, making it difficult to fully utilize the training images.
Unsupervised 3D Reconstruction Networks
The role of the 3D shape reconstructor is to reconstruct the 3D shape of an instance from its 2D feature points, and the rotation estimator infers the camera pose.
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
To handle the inherent ambiguity in human language commands, a suitable question which can resolve the ambiguity is generated.
Deep Pose Consensus Networks
In this paper, to resolve this issue, we propose a multiple-partial-hypothesis-based framework for the problem of estimating 3D human pose from a single image, which can be fine-tuned in an end-to-end fashion.
