Pose Prediction
57 papers with code • 3 benchmarks • 8 datasets
Pose prediction is to predict future poses given a window of previous poses.
Datasets
Latest papers with no code
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.
PoseGPT: Chatting about 3D Human Pose
We introduce PoseGPT, a framework employing Large Language Models (LLMs) to understand and reason about 3D human poses from images or textual descriptions.
Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement
In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion.