Search Results for author:
Found 8 papers, 7 papers with code
ShapeBoost: Boosting Human Shape Estimation with Part-Based Parameterization and Clothing-Preserving Augmentation
Accurate human shape recovery from a monocular RGB image is a challenging task because humans come in different shapes and sizes and wear different clothes.
Domain Invariant Learning for Gaussian Processes and Bayesian Exploration
We further demonstrate the effectiveness of the DIL-GP Bayesian optimization method on a PID parameters tuning experiment for a quadrotor.
NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation
In this work, we present NIKI (Neural Inverse Kinematics with Invertible Neural Network), which models bi-directional errors to improve the robustness to occlusions and obtain pixel-aligned accuracy.
Ranked #1 on
3D Human Pose Estimation
on AGORA
HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery
To address these issues, this paper presents a novel hybrid inverse kinematics solution, HybrIK, that integrates the merits of 3D keypoint estimation and body mesh recovery in a unified framework.
Ranked #1 on
3D Human Reconstruction
on AGORA
D&D: Learning Human Dynamics from Dynamic Camera
In this work, we present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the laws of physics to reconstruct 3D human motion from the in-the-wild videos with a moving camera.
Towards Using Clothes Style Transfer for Scenario-aware Person Video Generation
To tackle this problem, most recent AdaIN-based architectures are proposed to extract clothes and scenario features for generation.
Human Pose Regression with Residual Log-likelihood Estimation
In light of this, we propose a novel regression paradigm with Residual Log-likelihood Estimation (RLE) to capture the underlying output distribution.
Ranked #10 on
Pose Estimation
on COCO val2017
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation
We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods.
Ranked #3 on
3D Human Pose Estimation
on EMDB
