# Synthesizing Training Images for Boosting Human 3D Pose Estimation

arXiv: https://arxiv.org/abs/1604.02703 · HTML: https://mlsanity.com/paper/1604.02703/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2016.

## Built on by (most cited first, 30 of 52)

- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation](https://mlsanity.com/paper/2105.02465/) (2021)
- [ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis](https://mlsanity.com/paper/2109.05488/) (2021)
- [SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation](https://mlsanity.com/paper/2304.10417/) (2023)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Toward Accurate and Realistic Outfits Visualization with Attention to Details](https://mlsanity.com/paper/2106.06593/) (2021)
- [AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation](https://mlsanity.com/paper/2112.11593/) (2021)
- [PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision](https://mlsanity.com/paper/2203.15625/) (2022)
- [Image Compositing for Segmentation of Surgical Tools without Manual Annotations](https://mlsanity.com/paper/2102.09528/) (2021)
- [EgoEnv: Human-centric environment representations from egocentric video](https://mlsanity.com/paper/2207.11365/) (2022)
- [BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image](https://mlsanity.com/paper/2105.09936/) (2021)
- [Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose Estimation](https://mlsanity.com/paper/2303.16456/) (2023)
- [CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes](https://mlsanity.com/paper/2105.05497/) (2021)
- [Cloth Interactive Transformer for Virtual Try-On](https://mlsanity.com/paper/2104.05519/) (2021)
- [PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation](https://mlsanity.com/paper/2208.07755/) (2022)
- [Leveraging MoCap Data for Human Mesh Recovery](https://mlsanity.com/paper/2110.09243/) (2021)
- [Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery](https://mlsanity.com/paper/2206.10457/) (2022)
- [SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation](https://mlsanity.com/paper/2409.11682/) (2024)
- [im2nerf: Image to Neural Radiance Field in the Wild](https://mlsanity.com/paper/2209.04061/) (2022)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [SyDog: A Synthetic Dog Dataset for Improved 2D Pose Estimation](https://mlsanity.com/paper/2108.00249/) (2021)
- [Enhanced 3D Human Pose Estimation from Videos by using Attention-Based Neural Network with Dilated Convolutions](https://mlsanity.com/paper/2103.03170/) (2021)
- [Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge](https://mlsanity.com/paper/2211.11983/) (2022)
- [CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations](https://mlsanity.com/paper/2301.02979/) (2023)
- [Neural 3D Clothes Retargeting from a Single Image](https://mlsanity.com/paper/2102.00062/) (2021)
- [Synthetic training data generation for deep learning based quality inspection](https://mlsanity.com/paper/2104.02980/) (2021)
- [CondiMen: Conditional Multi-Person Mesh Recovery](https://mlsanity.com/paper/2412.13058/) (2024)
- [Limb-Aware Virtual Try-On Network with Progressive Clothing Warping](https://mlsanity.com/paper/2503.14074/) (2025)
- [PoseGU: 3D Human Pose Estimation with Novel Human Pose Generator and Unbiased Learning](https://mlsanity.com/paper/2207.03618/) (2022)
- [Alice Benchmarks: Connecting Real World Re-Identification with the Synthetic](https://mlsanity.com/paper/2310.04416/) (2023)

## Abstract (arXiv)

Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-to-end solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. Our work is a systematic study along this road. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. Furthermore, we explore domain adaptation for bridging the gap between our synthetic training images and real testing photos. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.