Search Results for author:
Found 9 papers, 5 papers with code
Learning Anchor Transformations for 3D Garment Animation
Once the anchor transformations are found, per-vertex nonlinear displacements of the garment template can be regressed in a canonical space, which reduces the complexity of deformation space learning.
Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry
Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing.
Truncate-Split-Contrast: A Framework for Learning from Mislabeled Videos
Thanks to Noise Contrastive Learning, the average classification accuracy improvement on Mini-Kinetics and Sth-Sth-V1 is over 1. 6\%.
LocVTP: Video-Text Pre-training for Temporal Localization
To further enhance the temporal reasoning ability of the learned feature, we propose a context projection head and a temporal aware contrastive loss to perceive the contextual relationships.
Unsupervised Pre-training for Temporal Action Localization Tasks
These pre-trained models can be sub-optimal for temporal localization tasks due to the inherent discrepancy between video-level classification and clip-level localization.
Temporal Distinct Representation Learning for Action Recognition
Motivated by the previous success of Two-Dimensional Convolutional Neural Network (2D CNN) on image recognition, researchers endeavor to leverage it to characterize videos.
Deformable Pose Traversal Convolution for 3D Action and Gesture Recognition
This deformable convolution can better utilize contextual joints for action and gesture recognition and is more robust to noisy joints.
Hand PointNet: 3D Hand Pose Estimation Using Point Sets
Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images.
Ranked #7 on
Hand Pose Estimation
on HANDS 2017
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action Recognition
Moreover, by identifying key skeleton joints and temporal stages for each action class, our ST-NBNN can capture the essential spatio-temporal patterns that play key roles of recognizing actions, which is not always achievable by using end-to-end models.
