# DiffMesh: A Motion-aware Diffusion Framework for Human Mesh Recovery from Videos

arXiv: https://arxiv.org/abs/2303.13397 · HTML: https://mlsanity.com/paper/2303.13397/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- First posted on arXiv 23 Mar 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Built on by (most cited first, 6 of 6)

- [DiSRT-In-Bed: Diffusion-Based Sim-to-Real Transfer Framework for In-Bed Human Mesh Recovery](https://mlsanity.com/paper/2504.03006/) (2025)
- [MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation](https://mlsanity.com/paper/2511.19326/) (2025)
- [DiffProxy: Multi-View Human Mesh Recovery via Diffusion-Generated Dense Proxies](https://mlsanity.com/paper/2601.02267/) (2026)
- [Hyperbolic Space Learning Method Leveraging Temporal Motion Priors for Human Mesh Recovery](https://mlsanity.com/paper/2510.18256/) (2025)
- [Latent-Info and Low-Dimensional Learning for Human Mesh Recovery and Parallel Optimization](https://mlsanity.com/paper/2510.18267/) (2025)
- [InterEdit: Navigating Text-Guided 3D Dyadic Human Motion Editing](https://mlsanity.com/paper/2603.13082/) (2026)

## Abstract (arXiv)

Human mesh recovery (HMR) provides rich human body information for various real-world applications. While image-based HMR methods have achieved impressive results, they often struggle to recover humans in dynamic scenarios, leading to temporal inconsistencies and non-smooth 3D motion predictions due to the absence of human motion. In contrast, video-based approaches leverage temporal information to mitigate this issue. In this paper, we present DiffMesh, an innovative motion-aware Diffusion-like framework for video-based HMR. DiffMesh establishes a bridge between diffusion models and human motion, efficiently generating accurate and smooth output mesh sequences by incorporating human motion within the forward process and reverse process in the diffusion model. Extensive experiments are conducted on the widely used datasets (Human3.6M citeh36mpami and 3DPW citepw3d2018), which demonstrate the effectiveness and efficiency of our DiffMesh. Visual comparisons in real-world scenarios further highlight DiffMesh's suitability for practical applications.