# Generating Multiple Diverse Hypotheses for Human 3D Pose Consistent with 2D Joint Detections

arXiv: https://arxiv.org/abs/1702.02258 · HTML: https://mlsanity.com/paper/1702.02258/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2017.

## Built on by (most cited first, 30 of 30)

- [Probabilistic Modeling for Human Mesh Recovery](https://mlsanity.com/paper/2108.11944/) (2021)
- [MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation](https://mlsanity.com/paper/2111.12707/) (2021)
- [Probabilistic Monocular 3D Human Pose Estimation with Normalizing Flows](https://mlsanity.com/paper/2107.13788/) (2021)
- [Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation](https://mlsanity.com/paper/2303.11579/) (2023)
- [DiffPose: Multi-hypothesis Human Pose Estimation using Diffusion models](https://mlsanity.com/paper/2211.16487/) (2022)
- [GFPose: Learning 3D Human Pose Prior with Gradient Fields](https://mlsanity.com/paper/2212.08641/) (2022)
- [Estimating Egocentric 3D Human Pose in Global Space](https://mlsanity.com/paper/2104.13454/) (2021)
- [Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild](https://mlsanity.com/paper/2110.00990/) (2021)
- [Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views](https://mlsanity.com/paper/2304.06024/) (2023)
- [DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2212.02796/) (2022)
- [Ego3DPose: Capturing 3D Cues from Binocular Egocentric Views](https://mlsanity.com/paper/2309.11962/) (2023)
- [Generative Approach for Probabilistic Human Mesh Recovery using Diffusion Models](https://mlsanity.com/paper/2308.02963/) (2023)
- [MEGA: Masked Generative Autoencoder for Human Mesh Recovery](https://mlsanity.com/paper/2405.18839/) (2024)
- [PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2308.09678/) (2023)
- [Explicit Occlusion Reasoning for Multi-person 3D Human Pose Estimation](https://mlsanity.com/paper/2208.00090/) (2022)
- [Dynamic Graph Reasoning for Multi-person 3D Pose Estimation](https://mlsanity.com/paper/2207.11341/) (2022)
- [Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions](https://mlsanity.com/paper/2210.11179/) (2022)
- [Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge](https://mlsanity.com/paper/2211.11983/) (2022)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [CondiMen: Conditional Multi-Person Mesh Recovery](https://mlsanity.com/paper/2412.13058/) (2024)
- [Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery](https://mlsanity.com/paper/2411.16289/) (2024)
- [FMPose3D: monocular 3D pose estimation via flow matching](https://mlsanity.com/paper/2602.05755/) (2026)
- [Physics-Guided Human Motion Capture with Pose Probability Modeling](https://mlsanity.com/paper/2308.09910/) (2023)
- [Multimodal Active Measurement for Human Mesh Recovery in Close Proximity](https://mlsanity.com/paper/2310.08116/) (2023)
- [Flow Matching for Probabilistic Monocular 3D Human Pose Estimation](https://mlsanity.com/paper/2601.16763/) (2026)
- [On the role of depth predictions for 3D human pose estimation](https://mlsanity.com/paper/2103.02521/) (2021)
- [Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose Data](https://mlsanity.com/paper/2105.10837/) (2021)
- [Probablistic Restoration with Adaptive Noise Sampling for 3D Human Pose Estimation](https://mlsanity.com/paper/2405.02114/) (2024)
- [Social EgoMesh Estimation](https://mlsanity.com/paper/2411.04598/) (2024)
- [CQF-HMR: Continuous Quaternion Flows for Probabilistic 3D Human Mesh Recovery from a Single Image](https://mlsanity.com/paper/2609.00995/) (2026)

## Abstract (arXiv)

We propose a method to generate multiple diverse and valid human pose hypotheses in 3D all consistent with the 2D detection of joints in a monocular RGB image. We use a novel generative model uniform (unbiased) in the space of anatomically plausible 3D poses. Our model is compositional (produces a pose by combining parts) and since it is restricted only by anatomical constraints it can generalize to every plausible human 3D pose. Removing the model bias intrinsically helps to generate more diverse 3D pose hypotheses. We argue that generating multiple pose hypotheses is more reasonable than generating only a single 3D pose based on the 2D joint detection given the depth ambiguity and the uncertainty due to occlusion and imperfect 2D joint detection. We hope that the idea of generating multiple consistent pose hypotheses can give rise to a new line of future work that has not received much attention in the literature. We used the Human3.6M dataset for empirical evaluation.