# AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation

arXiv: https://arxiv.org/abs/2403.17934 · HTML: https://mlsanity.com/paper/2403.17934/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 59 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [PARE: Part Attention Regressor for 3D Human Body Estimation](https://mlsanity.com/paper/2104.08527/) (2021)
- [ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation](https://mlsanity.com/paper/2204.13662/) (2022)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [Convolutional Mesh Regression for Single-Image Human Shape Reconstruction](https://mlsanity.com/paper/1905.03244/) (2019)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation](https://mlsanity.com/paper/2208.00571/) (2022)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [Monocular, One-stage, Regression of Multiple 3D People](https://mlsanity.com/paper/2008.12272/) (2020)
- [AGORA: Avatars in Geography Optimized for Regression Analysis](https://mlsanity.com/paper/2104.14643/) (2021)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [Monocular Expressive Body Regression through Body-Driven Attention](https://mlsanity.com/paper/2008.09062/) (2020)
- [SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2309.17448/) (2023)
- [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)
- [EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices](https://mlsanity.com/paper/2112.07642/) (2021)
- [Collaborative Regression of Expressive Bodies using Moderation](https://mlsanity.com/paper/2105.05301/) (2021)

## Built on by (most cited first, 30 of 42)

- [OmniRe: Omni Urban Scene Reconstruction](https://mlsanity.com/paper/2408.16760/) (2024)
- [PromptHMR: Promptable Human Mesh Recovery](https://mlsanity.com/paper/2504.06397/) (2025)
- [SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2501.09782/) (2025)
- [Vid2Avatar-Pro: Authentic Avatar from Videos in the Wild via Universal Prior](https://mlsanity.com/paper/2503.01610/) (2025)
- [UniTalker: Scaling up Audio-Driven 3D Facial Animation through A Unified Model](https://mlsanity.com/paper/2408.00762/) (2024)
- [InteractVLM: 3D Interaction Reasoning from 2D Foundational Models](https://mlsanity.com/paper/2504.05303/) (2025)
- [UniPose: A Unified Multimodal Framework for Human Pose Comprehension, Generation and Editing](https://mlsanity.com/paper/2411.16781/) (2024)
- [SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens](https://mlsanity.com/paper/2411.19824/) (2024)
- [SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation](https://mlsanity.com/paper/2411.19921/) (2024)
- [Human Mesh Modeling for Anny Body](https://mlsanity.com/paper/2511.03589/) (2025)
- [MoCapAnything: Unified 3D Motion Capture for Arbitrary Skeletons from Monocular Videos](https://mlsanity.com/paper/2512.10881/) (2025)
- [HandOS: 3D Hand Reconstruction in One Stage](https://mlsanity.com/paper/2412.01537/) (2024)
- [HumanRAM: Feed-forward Human Reconstruction and Animation Model using Transformers](https://mlsanity.com/paper/2506.03118/) (2025)
- [ADHMR: Aligning Diffusion-based Human Mesh Recovery via Direct Preference Optimization](https://mlsanity.com/paper/2505.10250/) (2025)
- [CondiMen: Conditional Multi-Person Mesh Recovery](https://mlsanity.com/paper/2412.13058/) (2024)
- [Towards Metric-Aware Multi-Person Mesh Recovery by Jointly Optimizing Human Crowd in Camera Space](https://mlsanity.com/paper/2511.13282/) (2025)
- [Efficient and Scalable Monocular Human-Object Interaction Motion Reconstruction](https://mlsanity.com/paper/2512.00960/) (2025)
- [PEAR: Pixel-aligned Expressive humAn mesh Recovery](https://mlsanity.com/paper/2601.22693/) (2026)
- [HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery](https://mlsanity.com/paper/2412.04456/) (2024)
- [MeshMamba: State Space Models for Articulated 3D Mesh Generation and Reconstruction](https://mlsanity.com/paper/2507.15212/) (2025)
- [PHD: Personalized 3D Human Body Fitting with Point Diffusion](https://mlsanity.com/paper/2508.21257/) (2025)
- [Enhancing Hands in 3D Whole-Body Pose Estimation with Conditional Hands Modulator](https://mlsanity.com/paper/2603.14726/) (2026)
- [Multi-HMR 2: Multi-Person Camera-Centric Human Detection, Mesh Recovery and Tracking](https://mlsanity.com/paper/2606.14841/) (2026)
- [Ask, Pose, Unite: Scaling Data Acquisition for Close Interactions with Vision Language Models](https://mlsanity.com/paper/2410.00309/) (2024)
- [DexAvatar: 3D Sign Language Reconstruction with Hand and Body Pose Priors](https://mlsanity.com/paper/2512.21054/) (2025)
- [Anny-Fit: All-Age Human Mesh Recovery](https://mlsanity.com/paper/2605.04728/) (2026)
- [DanceHMR: Hand-Aware Whole-Body Human Mesh Recovery from Monocular Videos](https://mlsanity.com/paper/2605.18102/) (2026)
- [DETRAM: End-to-end DEtection, Tracking and Recovery of HumAn Meshes](https://mlsanity.com/paper/2607.09089/) (2026)
- [Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation](https://mlsanity.com/paper/2405.11448/) (2024)
- [Mocap-2-to-3: Multi-view Lifting for Monocular Motion Recovery with 2D Pretraining](https://mlsanity.com/paper/2503.03222/) (2025)

## Abstract (arXiv)

Expressive human pose and shape estimation (a.k.a. 3D whole-body mesh recovery) involves the human body, hand, and expression estimation. Most existing methods have tackled this task in a two-stage manner, first detecting the human body part with an off-the-shelf detection model and inferring the different human body parts individually. Despite the impressive results achieved, these methods suffer from 1) loss of valuable contextual information via cropping, 2) introducing distractions, and 3) lacking inter-association among different persons and body parts, inevitably causing performance degradation, especially for crowded scenes. To address these issues, we introduce a novel all-in-one-stage framework, AiOS, for multiple expressive human pose and shape recovery without an additional human detection step. Specifically, our method is built upon DETR, which treats multi-person whole-body mesh recovery task as a progressive set prediction problem with various sequential detection. We devise the decoder tokens and extend them to our task. Specifically, we first employ a human token to probe a human location in the image and encode global features for each instance, which provides a coarse location for the later transformer block. Then, we introduce a joint-related token to probe the human joint in the image and encoder a fine-grained local feature, which collaborates with the global feature to regress the whole-body mesh. This straightforward but effective model outperforms previous state-of-the-art methods by a 9% reduction in NMVE on AGORA, a 30% reduction in PVE on EHF, a 10% reduction in PVE on ARCTIC, and a 3% reduction in PVE on EgoBody.