Multi-Hypotheses 3D Human Pose Estimation
7 papers with code • 2 benchmarks • 2 datasets
Most implemented papers
Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network
We argue that 3D human pose estimation from a monocular input is an inverse problem where multiple feasible solutions can exist.
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D.
Weakly Supervised Generative Network for Multiple 3D Human Pose Hypotheses
In this paper, we propose a weakly supervised deep generative network to address the inverse problem and circumvent the need for ground truth 2D-to-3D correspondences.
Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows
3D human pose estimation from monocular images is a highly ill-posed problem due to depth ambiguities and occlusions.
Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions
We evaluate cGNF on the Human~3. 6M dataset and show that cGNF provides a well-calibrated distribution estimate while being close to state-of-the-art in terms of overall minMPJPE.
GFPose: Learning 3D Human Pose Prior with Gradient Fields
During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework.
Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation
On the other hand, JPMA is proposed to assemble multiple hypotheses generated by D3DP into a single 3D pose for practical use.