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Found 11 papers, 1 papers with code
Adaptive Computationally Efficient Network for Monocular 3D Hand Pose Estimation
A novel model, called Adaptive Computationally Efficient (ACE) network, is proposed, which takes advantage of a Gaussian kernel based Gate Module to dynamically switch the computation between a light model and a heavy network for feature extraction.
SM$^3$: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects
Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics.
MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers
Recent advances in generative AI have significantly enhanced image and video editing, particularly in the context of text prompt control.
DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene.
GradMDM: Adversarial Attack on Dynamic Networks
Dynamic neural networks can greatly reduce computation redundancy without compromising accuracy by adapting their structures based on the input.
Progressive Channel-Shrinking Network
Currently, salience-based channel pruning makes continuous breakthroughs in network compression.
System-status-aware Adaptive Network for Online Streaming Video Understanding
Recent years have witnessed great progress in deep neural networks for real-time applications.
Lightweight Estimation of Hand Mesh and Biomechanically Feasible Kinematic Parameters
Furthermore, we introduce an inverted kinematic(IK) network to translate the estimated hand mesh to a biomechanically feasible set of joint rotation parameters, which is necessary for applications that leverage pose estimation for controlling robotic hands.
DiffPose: Toward More Reliable 3D Pose Estimation
Monocular 3D human pose estimation is quite challenging due to the inherent ambiguity and occlusion, which often lead to high uncertainty and indeterminacy.
Ranked #11 on
3D Human Pose Estimation
on MPI-INF-3DHP
Meta Agent Teaming Active Learning for Pose Estimation
The existing pose estimation approaches often require a large number of annotated images to attain good estimation performance, which are laborious to acquire.
Motion Adaptive Pose Estimation From Compressed Videos
A novel model, called Motion Adaptive Pose Net is proposed to exploit the compressed streams to efficiently decode pose sequences from videos.
