Pose Prediction
58 papers with code • 3 benchmarks • 8 datasets
Pose prediction is to predict future poses given a window of previous poses.
Benchmarks
These leaderboards are used to track progress in Pose Prediction
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
Datasets
NTU RGB+D
G3D
Expi
SportsPose
Drunkard's Dataset
InfiniteRep
Latest papers with no code
Deep SE(3)-Equivariant Geometric Reasoning for Precise Placement Tasks
In this work, we propose a method for precise relative pose prediction which is provably SE(3)-equivariant, can be learned from only a few demonstrations, and can generalize across variations in a class of objects.
Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos
We present a method to build animatable dog avatars from monocular videos.
MatchU: Matching Unseen Objects for 6D Pose Estimation from RGB-D Images
MatchU is a generic approach that fuses 2D texture and 3D geometric cues for 6D pose prediction of unseen objects.
Dynamic Anchor Selection and Real-Time Pose Prediction for Ultra-wideband Tagless Gate
DynaPose is based on line-of-sight (LOS) and non-LOS (NLOS) classification using deep learning for anchor selection and pose prediction.
Towards a large-scale fused and labeled dataset of human pose while interacting with robots in shared urban areas
In contrast, YOLOv7 performs better in single-person estimation (NCLT seq 2) and outdoor scenarios (MOT17 seq1), achieving MSJE values of 5. 29 and 3. 38, respectively.
DeepRLI: A Multi-objective Framework for Universal Protein--Ligand Interaction Prediction
In order to equip the model to generalize to conformations beyond the confines of crystal structures and to adapt to molecular docking and virtual screening tasks, we propose a multi-objective strategy, that is, the model outputs three scores for scoring and ranking, docking, and screening, and the training process optimizes these three objectives simultaneously.
Collaboratively Self-supervised Video Representation Learning for Action Recognition
Considering the close connection between action recognition and human pose estimation, we design a Collaboratively Self-supervised Video Representation (CSVR) learning framework specific to action recognition by jointly considering generative pose prediction and discriminative context matching as pretext tasks.
ManipLLM: Embodied Multimodal Large Language Model for Object-Centric Robotic Manipulation
By fine-tuning the injected adapters, we preserve the inherent common sense and reasoning ability of the MLLMs while equipping them with the ability for manipulation.
S2P3: Self-Supervised Polarimetric Pose Prediction
The novel training paradigm comprises 1) a physical model to extract geometric information of polarized light, 2) a teacher-student knowledge distillation scheme and 3) a self-supervised loss formulation through differentiable rendering and an invertible physical constraint.
ChatPose: Chatting about 3D Human Pose
Additionally, ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses, leading to two advanced tasks: speculative pose generation and reasoning about pose estimation.
