# Human3R: Everyone Everywhere All at Once

arXiv: https://arxiv.org/abs/2510.06219 · HTML: https://mlsanity.com/paper/2510.06219/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 94 of its 116 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 30 of 94)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Exploring Plain Vision Transformer Backbones for Object Detection](https://mlsanity.com/paper/2203.16527/) (2022)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://mlsanity.com/paper/2412.14171/) (2024)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Continuous 3D Perception Model with Persistent State](https://mlsanity.com/paper/2501.12387/) (2025)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion](https://mlsanity.com/paper/2410.03825/) (2024)

## Built on by (most cited first, 30 of 37)

- [VGGT-Omega](https://mlsanity.com/paper/2605.15195/) (2026)
- [PhySIC: Physically Plausible 3D Human-Scene Interaction and Contact from a Single Image](https://mlsanity.com/paper/2510.11649/) (2025)
- [TTSA3R: Training-Free Temporal-Spatial Adaptive Persistent State for Streaming 3D Reconstruction](https://mlsanity.com/paper/2601.22615/) (2026)
- [Vision Pretraining for Dense Spatial Perception](https://mlsanity.com/paper/2607.05247/) (2026)
- [Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective](https://mlsanity.com/paper/2604.14025/) (2026)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)
- [HGGT: Robust and Flexible 3D Hand Mesh Reconstruction from Uncalibrated Images](https://mlsanity.com/paper/2603.23997/) (2026)
- [ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine](https://mlsanity.com/paper/2607.28625/) (2026)
- [Real-Time Human Frontal View Synthesis from a Single Image](https://mlsanity.com/paper/2603.15433/) (2026)
- [HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions](https://mlsanity.com/paper/2603.15612/) (2026)
- [GRAFT: Geometric Refinement and Fitting Transformer for Human Scene Reconstruction](https://mlsanity.com/paper/2604.19624/) (2026)
- [R3: 3D Reconstruction via Relative Regression](https://mlsanity.com/paper/2605.26519/) (2026)
- [Scene and Human in One World: Reconstruction in a Feedforward Pass](https://mlsanity.com/paper/2606.27720/) (2026)
- [DiffProxy: Multi-View Human Mesh Recovery via Diffusion-Generated Dense Proxies](https://mlsanity.com/paper/2601.02267/) (2026)
- [Hand3R: Online 4D Hand-Scene Reconstruction in the Wild](https://mlsanity.com/paper/2602.03200/) (2026)
- [AHAP: Reconstructing Arbitrary Humans from Arbitrary Perspectives with Geometric Priors](https://mlsanity.com/paper/2602.23951/) (2026)
- [End-to-End Spatial-Temporal Transformer for Real-time 4D HOI Reconstruction](https://mlsanity.com/paper/2603.14435/) (2026)
- [InHabit: Leveraging Image Foundation Models for Scalable 3D Human Placement](https://mlsanity.com/paper/2604.19673/) (2026)
- [UniCon3R: Unified Contact-aware 4D Human-Scene Reconstruction from Monocular Video](https://mlsanity.com/paper/2604.19923/) (2026)
- [DanceHMR: Hand-Aware Whole-Body Human Mesh Recovery from Monocular Videos](https://mlsanity.com/paper/2605.18102/) (2026)
- [HiReFF: High-Resolution Feedforward Human Reconstruction from Uncalibrated Sparse-View Video](https://mlsanity.com/paper/2606.29333/) (2026)
- [ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video](https://mlsanity.com/paper/2607.17790/) (2026)
- [Bitbox: Behavioral Imaging Toolbox for Computational Analysis of Behavior from Videos](https://mlsanity.com/paper/2512.17655/) (2025)
- [HARMONI: Aligning Human and Scene Priors for Multi-View 4D Reconstruction](https://mlsanity.com/paper/2603.12789/) (2026)
- [ONE-SHOT: Compositional Human-Environment Video Synthesis via Spatial-Decoupled Motion Injection and Hybrid Context Integration](https://mlsanity.com/paper/2604.01043/) (2026)
- [Good Token Hunting: A Hitchhiker's Guide to Token Selection for Visual Geometry Transformers](https://mlsanity.com/paper/2605.23892/) (2026)
- [Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos](https://mlsanity.com/paper/2605.26879/) (2026)
- [TROPHIES: Temporal Reconstruction of Places, Humans, and Cameras from Multi-view Videos](https://mlsanity.com/paper/2606.02350/) (2026)
- [EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal](https://mlsanity.com/paper/2607.13472/) (2026)
- [Crowd4D: Scene-Aware Monocular 4D Crowd Reconstruction](https://mlsanity.com/paper/2607.19517/) (2026)

## Abstract (arXiv)

We present Human3R, a unified, feed-forward framework for online 4D human-scene reconstruction, in the world frame, from casually captured monocular videos. Unlike previous approaches that rely on multi-stage pipelines, iterative contact-aware refinement between humans and scenes, and heavy dependencies, e.g., human detection, depth estimation, and SLAM pre-processing, Human3R jointly recovers global multi-person SMPL-X bodies ("everyone"), dense 3D scene ("everywhere"), and camera trajectories in a single forward pass ("all-at-once"). Our method builds upon the 4D online reconstruction model CUT3R, and uses parameter-efficient visual prompt tuning, to strive to preserve CUT3R's rich spatiotemporal priors, while enabling direct readout of multiple SMPL-X bodies. Human3R is a unified model that eliminates heavy dependencies and iterative refinement. After being trained on the relatively small-scale synthetic dataset BEDLAM for just one day on one GPU, it achieves superior performance with remarkable efficiency: it reconstructs multiple humans in a one-shot manner, along with 3D scenes, in one stage, in real-time (15 FPS) with a low memory footprint (8 GB). Extensive experiments demonstrate that Human3R delivers state-of-the-art or competitive performance across tasks, including global human motion estimation, local human mesh recovery, video depth estimation, and camera pose estimation, with a single unified model. We hope that Human3R will serve as a simple yet strong baseline, which can be easily adapted for downstream applications. Code, models and 4D interactive demos are available at https://fanegg.github.io/Human3R/.