# Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation

arXiv: https://arxiv.org/abs/2407.07532 · HTML: https://mlsanity.com/paper/2407.07532/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 103 of its 132 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 30 of 103)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [HyperNetworks](https://mlsanity.com/paper/1609.09106/) (2016)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision](https://mlsanity.com/paper/1611.09813/) (2016)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [Learning from Synthetic Humans](https://mlsanity.com/paper/1701.01370/) (2017)
- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
- [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)
- [I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image](https://mlsanity.com/paper/2008.03713/) (2020)
- [Convolutional Mesh Regression for Single-Image Human Shape Reconstruction](https://mlsanity.com/paper/1905.03244/) (2019)
- [Neural Unsigned Distance Fields for Implicit Function Learning](https://mlsanity.com/paper/2010.13938/) (2020)
- [Mesh Graphormer](https://mlsanity.com/paper/2104.00272/) (2021)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop](https://mlsanity.com/paper/2103.16507/) (2021)
- [BEHAVE: Dataset and Method for Tracking Human Object Interactions](https://mlsanity.com/paper/2204.06950/) (2022)
- [Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose](https://mlsanity.com/paper/2008.09047/) (2020)

## Built on by (most cited first, 30 of 59)

- [SAM 3D Body: Robust Full-Body Human Mesh Recovery](https://mlsanity.com/paper/2602.15989/) (2026)
- [IDOL: Instant Photorealistic 3D Human Creation from a Single Image](https://mlsanity.com/paper/2412.14963/) (2024)
- [GENMO: A GENeralist Model for Human MOtion](https://mlsanity.com/paper/2505.01425/) (2025)
- [Human3R: Everyone Everywhere All at Once](https://mlsanity.com/paper/2510.06219/) (2025)
- [Reconstructing Humans with a Biomechanically Accurate Skeleton](https://mlsanity.com/paper/2503.21751/) (2025)
- [GUAVA: Generalizable Upper Body 3D Gaussian Avatar](https://mlsanity.com/paper/2505.03351/) (2025)
- [InteractVLM: 3D Interaction Reasoning from 2D Foundational Models](https://mlsanity.com/paper/2504.05303/) (2025)
- [Reconstructing 4D Spatial Intelligence: A Survey](https://mlsanity.com/paper/2507.21045/) (2025)
- [PhySIC: Physically Plausible 3D Human-Scene Interaction and Contact from a Single Image](https://mlsanity.com/paper/2510.11649/) (2025)
- [CARI4D: Category Agnostic 4D Reconstruction of Human-Object Interaction](https://mlsanity.com/paper/2512.11988/) (2025)
- [CoMotion: Concurrent Multi-person 3D Motion](https://mlsanity.com/paper/2504.12186/) (2025)
- [SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations](https://mlsanity.com/paper/2512.05905/) (2025)
- [UniSH: Unifying Scene and Human Reconstruction in a Feed-Forward Pass](https://mlsanity.com/paper/2601.01222/) (2026)
- [SCAIL-2: Unifying Controlled Character Animation with End-to-End In-Context Conditioning](https://mlsanity.com/paper/2606.10804/) (2026)
- [DuoMo: Dual Motion Diffusion for World-Space Human Reconstruction](https://mlsanity.com/paper/2603.03265/) (2026)
- [SOMA: Unifying Parametric Human Body Models](https://mlsanity.com/paper/2603.16858/) (2026)
- [CondiMen: Conditional Multi-Person Mesh Recovery](https://mlsanity.com/paper/2412.13058/) (2024)
- [ECHO: Ego-Centric modeling of Human-Object interactions](https://mlsanity.com/paper/2508.21556/) (2025)
- [MetricHMSR:Metric Human Mesh and Scene Recovery from Monocular Images](https://mlsanity.com/paper/2506.09919/) (2025)
- [Kineo: Calibration-Free Metric Motion Capture From Sparse RGB Cameras](https://mlsanity.com/paper/2510.24464/) (2025)
- [Fast SAM 3D Body: Accelerating SAM 3D Body for Real-Time Full-Body Human Mesh Recovery](https://mlsanity.com/paper/2603.15603/) (2026)
- [Dance Style Recognition Using Laban Movement Analysis](https://mlsanity.com/paper/2504.21166/) (2025)
- [MTVCraft: Tokenizing 4D Motion for Arbitrary Character Animation](https://mlsanity.com/paper/2505.10238/) (2025)
- [InfiniHuman: Infinite 3D Human Creation with Precise Control](https://mlsanity.com/paper/2510.11650/) (2025)
- [Towards Fine-Grained Human Motion Video Captioning](https://mlsanity.com/paper/2510.24767/) (2025)
- [Efficient and Scalable Monocular Human-Object Interaction Motion Reconstruction](https://mlsanity.com/paper/2512.00960/) (2025)
- [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)
- [VLM-Guided Group Preference Alignment for Diffusion-based Human Mesh Recovery](https://mlsanity.com/paper/2602.19180/) (2026)
- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)

## Abstract (arXiv)

With the explosive growth of available training data, single-image 3D human modeling is ahead of a transition to a data-centric paradigm. A key to successfully exploiting data scale is to design flexible models that can be supervised from various heterogeneous data sources produced by different researchers or vendors. To this end, we propose a simple yet powerful paradigm for seamlessly unifying different human pose and shape-related tasks and datasets. Our formulation is centered on the ability -- both at training and test time -- to query any arbitrary point of the human volume, and obtain its estimated location in 3D. We achieve this by learning a continuous neural field of body point localizer functions, each of which is a differently parameterized 3D heatmap-based convolutional point localizer (detector). For generating parametric output, we propose an efficient post-processing step for fitting SMPL-family body models to nonparametric joint and vertex predictions. With this approach, we can naturally exploit differently annotated data sources including mesh, 2D/3D skeleton and dense pose, without having to convert between them, and thereby train large-scale 3D human mesh and skeleton estimation models that considerably outperform the state-of-the-art on several public benchmarks including 3DPW, EMDB, EHF, SSP-3D and AGORA.