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Found 11 papers, 0 papers with code
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation
This paper concentrates on an unexplored yet meaningful task, i. e., interactive point cloud semantic segmentation, which assigns high-quality semantic labels to all points in a scene with user corrective clicks.
VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting
Sparse meteorological forecasting is indispensable for fine-grained weather forecasting and deserves extensive attention.
Enhanced Fine-grained Motion Diffusion for Text-driven Human Motion Synthesis
The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently.
Meta-Auxiliary Learning for Adaptive Human Pose Prediction
Predicting high-fidelity future human poses, from a historically observed sequence, is decisive for intelligent robots to interact with humans.
DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback
Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration.
Test-time Personalizable Forecasting of 3D Human Poses
Current motion forecasting approaches typically train a deep end-to-end model from the source domain data, and then apply it directly to target subjects.
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds
To address the issues, we propose a simple but efficient augmentation fusion contrastive learning framework to combine data augmentations in Euclidean space and feature augmentations in feature space.
Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations
Secondly, the WHCN takes the hypergraph as input and learns to predict high-precision point-level pseudo labels by label propagation.
AS-PD: An Arbitrary-Size Downsampling Framework for Point Clouds
Given an input point cloud of arbitrary size, we first perform a task-agnostic pre-sampling on the input point cloud to a specified sample size.
Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction
This process offers a natural way to obtain the "whitened" latents without any trainable parameters, and human motion prediction can be regarded as the reverse diffusion process that converts the noise distribution into realistic future motions conditioned on the observed sequence.
Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Human Motion Prediction
Previous works on human motion prediction follow the pattern of building a mapping relation between the sequence observed and the one to be predicted.
