Pose Prediction
57 papers with code • 3 benchmarks • 8 datasets
Pose prediction is to predict future poses given a window of previous poses.
Datasets
Most implemented papers
PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling
Different pre-screening methods have been developed for rapid screening, but there is still a lack of structure-based methods applicable to various proteins that perform protein-ligand binding conformation prediction and scoring in an extremely short time.
SketchParse : Towards Rich Descriptions for Poorly Drawn Sketches using Multi-Task Hierarchical Deep Networks
We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches.
Predcnn: Predictive learning with cascade convolutions
Predicting future frames in videos remains an unsolved but challenging problem.
Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition
We validate AS-GCN in action recognition using two skeleton data sets, NTU-RGB+D and Kinetics.
CullNet: Calibrated and Pose Aware Confidence Scores for Object Pose Estimation
We present a new approach for a single view, image-based object pose estimation.
Dynamic multi-object Gaussian process models: A framework for data-driven functional modelling of human joints
In this paper, we propose a new framework for dynamic multi-object statistical modelling framework for the analysis of human joints in a continuous domain.
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction
The use of drug combinations often leads to polypharmacy side effects (POSE).
Learning 3D Part Assembly from a Single Image
Autonomous assembly is a crucial capability for robots in many applications.
Monocular Camera Localization in Prior LiDAR Maps with 2D-3D Line Correspondences
With the pose prediction from VIO, we can efficiently obtain coarse 2D-3D line correspondences.
RGBD-Dog: Predicting Canine Pose from RGBD Sensors
We evaluate our model on both synthetic and real RGBD images and compare our results to previously published work fitting canine models to images.