# Recurrent 3D Pose Sequence Machines

arXiv: https://arxiv.org/abs/1707.09695 · HTML: https://mlsanity.com/paper/1707.09695/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 12 of 12)

- [MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video](https://mlsanity.com/paper/2203.00859/) (2022)
- [Estimating Egocentric 3D Human Pose in Global Space](https://mlsanity.com/paper/2104.13454/) (2021)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Hand Image Understanding via Deep Multi-Task Learning](https://mlsanity.com/paper/2107.11646/) (2021)
- [Improving Robustness and Accuracy via Relative Information Encoding in 3D Human Pose Estimation](https://mlsanity.com/paper/2107.13994/) (2021)
- [Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos](https://mlsanity.com/paper/2109.07353/) (2021)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [ViewNet: Unsupervised Viewpoint Estimation from Conditional Generation](https://mlsanity.com/paper/2212.00435/) (2022)
- [Self-Supervised 3D Human Pose Estimation in Static Video Via Neural Rendering](https://mlsanity.com/paper/2210.04514/) (2022)
- [Representation Recycling for Streaming Video Analysis](https://mlsanity.com/paper/2204.13492/) (2022)
- [D3PRefiner: A Diffusion-based Denoise Method for 3D Human Pose Refinement](https://mlsanity.com/paper/2401.03914/) (2024)
- [NeRF-VPT: Learning Novel View Representations with Neural Radiance Fields via View Prompt Tuning](https://mlsanity.com/paper/2403.01325/) (2024)

## Abstract (arXiv)

3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery. It is thus critical to exploit rich spatial and temporal long-range dependencies among body joints for accurate 3D pose sequence prediction. Existing approaches usually manually design some elaborate prior terms and human body kinematic constraints for capturing structures, which are often insufficient to exploit all intrinsic structures and not scalable for all scenarios. In contrast, this paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically learn the image-dependent structural constraint and sequence-dependent temporal context by using a multi-stage sequential refinement. At each stage, our RPSM is composed of three modules to predict the 3D pose sequences based on the previously learned 2D pose representations and 3D poses: (i) a 2D pose module extracting the image-dependent pose representations, (ii) a 3D pose recurrent module regressing 3D poses and (iii) a feature adaption module serving as a bridge between module (i) and (ii) to enable the representation transformation from 2D to 3D domain. These three modules are then assembled into a sequential prediction framework to refine the predicted poses with multiple recurrent stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset show that our RPSM outperforms all state-of-the-art approaches for 3D pose estimation.