# Flowing ConvNets for Human Pose Estimation in Videos

arXiv: https://arxiv.org/abs/1506.02897 · HTML: https://mlsanity.com/paper/1506.02897/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2015.

## Built on by (most cited first, 30 of 55)

- [Deep Dual Consecutive Network for Human Pose Estimation](https://mlsanity.com/paper/2103.07254/) (2021)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking](https://mlsanity.com/paper/2106.03772/) (2021)
- [DiffPose: SpatioTemporal Diffusion Model for Video-Based Human Pose Estimation](https://mlsanity.com/paper/2307.16687/) (2023)
- [Test-Time Personalization with a Transformer for Human Pose Estimation](https://mlsanity.com/paper/2107.02133/) (2021)
- [Unsupervised Human Pose Estimation through Transforming Shape Templates](https://mlsanity.com/paper/2105.04154/) (2021)
- [ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search](https://mlsanity.com/paper/2105.10154/) (2021)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans](https://mlsanity.com/paper/2109.11941/) (2021)
- [Temporal Feature Alignment and Mutual Information Maximization for Video-Based Human Pose Estimation](https://mlsanity.com/paper/2203.15227/) (2022)
- [2D Human Pose Estimation: A Survey](https://mlsanity.com/paper/2204.07370/) (2022)
- [Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video](https://mlsanity.com/paper/2303.08475/) (2023)
- [Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes](https://mlsanity.com/paper/2403.16499/) (2024)
- [HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE](https://mlsanity.com/paper/2204.01565/) (2022)
- [Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation](https://mlsanity.com/paper/2207.03714/) (2022)
- [Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding Bridge](https://mlsanity.com/paper/2402.16050/) (2024)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images](https://mlsanity.com/paper/2105.09124/) (2021)
- [Privacy-Preserving In-Bed Pose Monitoring: A Fusion and Reconstruction Study](https://mlsanity.com/paper/2202.10704/) (2022)
- [End-to-end Weakly-supervised Single-stage Multiple 3D Hand Mesh Reconstruction from a Single RGB Image](https://mlsanity.com/paper/2204.08154/) (2022)
- [Human Pose-based Estimation, Tracking and Action Recognition with Deep Learning: A Survey](https://mlsanity.com/paper/2310.13039/) (2023)
- [Anatomizing Deep Learning Inference in Web Browsers](https://mlsanity.com/paper/2402.05981/) (2024)
- [Bottom-Up 2D Pose Estimation via Dual Anatomical Centers for Small-Scale Persons](https://mlsanity.com/paper/2208.11975/) (2022)
- [A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation](https://mlsanity.com/paper/2209.07034/) (2022)
- [Reciprocal Landmark Detection and Tracking with Extremely Few Annotations](https://mlsanity.com/paper/2101.11224/) (2021)
- [Single Person Pose Estimation: A Survey](https://mlsanity.com/paper/2109.10056/) (2021)
- [Feature Aggregation and Refinement Network for 2D AnatomicalLandmark Detection](https://mlsanity.com/paper/2111.00659/) (2021)
- [From Keypoints to Object Landmarks via Self-Training Correspondence: A novel approach to Unsupervised Landmark Discovery](https://mlsanity.com/paper/2205.15895/) (2022)
- [CNN-Based Action Recognition and Pose Estimation for Classifying Animal Behavior from Videos: A Survey](https://mlsanity.com/paper/2301.06187/) (2023)
- [GestSync: Determining who is speaking without a talking head](https://mlsanity.com/paper/2310.05304/) (2023)

## Abstract (arXiv)

The objective of this work is human pose estimation in videos, where multiple frames are available. We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow. To this end we propose a network architecture with the following novelties: (i) a deeper network than previously investigated for regressing heatmaps; (ii) spatial fusion layers that learn an implicit spatial model; (iii) optical flow is used to align heatmap predictions from neighbouring frames; and (iv) a final parametric pooling layer which learns to combine the aligned heatmaps into a pooled confidence map. We show that this architecture outperforms a number of others, including one that uses optical flow solely at the input layers, one that regresses joint coordinates directly, and one that predicts heatmaps without spatial fusion. The new architecture outperforms the state of the art by a large margin on three video pose estimation datasets, including the very challenging Poses in the Wild dataset, and outperforms other deep methods that don't use a graphical model on the single-image FLIC benchmark (and also Chen & Yuille and Tompson et al. in the high precision region).