# TexturePose: Supervising Human Mesh Estimation with Texture Consistency

arXiv: https://arxiv.org/abs/1910.11322 · HTML: https://mlsanity.com/paper/1910.11322/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 30 of 53)

- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [Probabilistic Modeling for Human Mesh Recovery](https://mlsanity.com/paper/2108.11944/) (2021)
- [GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Cameras](https://mlsanity.com/paper/2112.01524/) (2021)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [Putting People in their Place: Monocular Regression of 3D People in Depth](https://mlsanity.com/paper/2112.08274/) (2021)
- [Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimation](https://mlsanity.com/paper/2104.14639/) (2021)
- [TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D Environments](https://mlsanity.com/paper/2306.02850/) (2023)
- [Deep Regression on Manifolds: A 3D Rotation Case Study](https://mlsanity.com/paper/2103.16317/) (2021)
- [S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling](https://mlsanity.com/paper/2101.06571/) (2021)
- [Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration](https://mlsanity.com/paper/2103.02845/) (2021)
- [Human-Aware Object Placement for Visual Environment Reconstruction](https://mlsanity.com/paper/2203.03609/) (2022)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Hierarchical Kinematic Probability Distributions for 3D Human Shape and Pose Estimation from Images in the Wild](https://mlsanity.com/paper/2110.00990/) (2021)
- [TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer](https://mlsanity.com/paper/2211.10705/) (2022)
- [Probabilistic 3D Human Shape and Pose Estimation from Multiple Unconstrained Images in the Wild](https://mlsanity.com/paper/2103.10978/) (2021)
- [Adversarial Parametric Pose Prior](https://mlsanity.com/paper/2112.04203/) (2021)
- [Learned Vertex Descent: A New Direction for 3D Human Model Fitting](https://mlsanity.com/paper/2205.06254/) (2022)
- [PACE: Human and Camera Motion Estimation from in-the-wild Videos](https://mlsanity.com/paper/2310.13768/) (2023)
- [Probabilistic Human Mesh Recovery in 3D Scenes from Egocentric Views](https://mlsanity.com/paper/2304.06024/) (2023)
- [PSVT: End-to-End Multi-person 3D Pose and Shape Estimation with Progressive Video Transformers](https://mlsanity.com/paper/2303.09187/) (2023)
- [KAMA: 3D Keypoint Aware Body Mesh Articulation](https://mlsanity.com/paper/2104.13502/) (2021)
- [A Survey on Human-aware Robot Navigation](https://mlsanity.com/paper/2106.11650/) (2021)
- [Tracking People with 3D Representations](https://mlsanity.com/paper/2111.07868/) (2021)
- [Learning monocular 3D reconstruction of articulated categories from motion](https://mlsanity.com/paper/2103.16352/) (2021)
- [Learning Visibility for Robust Dense Human Body Estimation](https://mlsanity.com/paper/2208.10652/) (2022)
- [SeSDF: Self-evolved Signed Distance Field for Implicit 3D Clothed Human Reconstruction](https://mlsanity.com/paper/2304.00359/) (2023)
- [COIN: Control-Inpainting Diffusion Prior for Human and Camera Motion Estimation](https://mlsanity.com/paper/2408.16426/) (2024)
- [Leveraging MoCap Data for Human Mesh Recovery](https://mlsanity.com/paper/2110.09243/) (2021)
- [Crowd3D: Towards Hundreds of People Reconstruction from a Single Image](https://mlsanity.com/paper/2301.09376/) (2023)
- [Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement](https://mlsanity.com/paper/2210.13529/) (2022)

## Abstract (arXiv)

This work addresses the problem of model-based human pose estimation. Recent approaches have made significant progress towards regressing the parameters of parametric human body models directly from images. Because of the absence of images with 3D shape ground truth, relevant approaches rely on 2D annotations or sophisticated architecture designs. In this work, we advocate that there are more cues we can leverage, which are available for free in natural images, i.e., without getting more annotations, or modifying the network architecture. We propose a natural form of supervision, that capitalizes on the appearance constancy of a person among different frames (or viewpoints). This seemingly insignificant and often overlooked cue goes a long way for model-based pose estimation. The parametric model we employ allows us to compute a texture map for each frame. Assuming that the texture of the person does not change dramatically between frames, we can apply a novel texture consistency loss, which enforces that each point in the texture map has the same texture value across all frames. Since the texture is transferred in this common texture map space, no camera motion computation is necessary, or even an assumption of smoothness among frames. This makes our proposed supervision applicable in a variety of settings, ranging from monocular video, to multi-view images. We benchmark our approach against strong baselines that require the same or even more annotations that we do and we consistently outperform them. Simultaneously, we achieve state-of-the-art results among model-based pose estimation approaches in different benchmarks. The project website with videos, results, and code can be found at https://seas.upenn.edu/~pavlakos/projects/texturepose.