# MVHumanNet++: A Large-scale Dataset of Multi-view Daily Dressing Human Captures with Richer Annotations for 3D Human Digitization arXiv: https://arxiv.org/abs/2505.01838 ยท HTML: https://mlsanity.com/paper/2505.01838/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 82 of its 115 references that are in the map. - First posted on arXiv 3 May 2025. ## Builds on (most cited first, 30 of 82) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023) - [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018) - [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016) - [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022) - [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022) - [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019) - [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019) - [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) - [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023) - [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022) - [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023) - [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019) - [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021) - [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024) - [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016) - [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018) - [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023) - [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023) - [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019) - [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023) - [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021) - [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023) - [NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis](https://mlsanity.com/paper/1604.02808/) (2016) - [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020) - [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019) - [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020) ## Built on by (most cited first, 9 of 9) - [MV-Performer: Taming Video Diffusion Model for Faithful and Synchronized Multi-view Performer Synthesis](https://mlsanity.com/paper/2510.07190/) (2025) - [VolHuMe: a High-Resolution Large Scale Dataset of Volumetric Human Meshes](https://mlsanity.com/paper/2606.23062/) (2026) - [HiReFF: High-Resolution Feedforward Human Reconstruction from Uncalibrated Sparse-View Video](https://mlsanity.com/paper/2606.29333/) (2026) - [4DAnyone: Create Anyone in 4D from a Casual Monocular Video](https://mlsanity.com/paper/2608.20335/) (2026) - [MV-Fashion: Towards Enabling Virtual Try-On and Size Estimation with Multi-View Paired Data](https://mlsanity.com/paper/2603.08147/) (2026) - [ReImagine: Rethinking Controllable High-Quality Human Video Generation via Image-First Synthesis](https://mlsanity.com/paper/2604.19720/) (2026) - [Online Neural Space Time Memory for Dynamic Novel View Synthesis](https://mlsanity.com/paper/2607.15271/) (2026) - [4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans](https://mlsanity.com/paper/2607.27634/) (2026) - [AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Textures](https://mlsanity.com/paper/2608.19900/) (2026) ## Abstract (arXiv) In this era, the success of large language models and text-to-image models can be attributed to the driving force of large-scale datasets. However, in the realm of 3D vision, while significant progress has been achieved in object-centric tasks through large-scale datasets like Objaverse and MVImgNet, human-centric tasks have seen limited advancement, largely due to the absence of a comparable large-scale human dataset. To bridge this gap, we present MVHumanNet++, a dataset that comprises multi-view human action sequences of 4,500 human identities. The primary focus of our work is on collecting human data that features a large number of diverse identities and everyday clothing using multi-view human capture systems, which facilitates easily scalable data collection. Our dataset contains 9,000 daily outfits, 60,000 motion sequences and 645 million frames with extensive annotations, including human masks, camera parameters, 2D and 3D keypoints, SMPL/SMPLX parameters, and corresponding textual descriptions. Additionally, the proposed MVHumanNet++ dataset is enhanced with newly processed normal maps and depth maps, significantly expanding its applicability and utility for advanced human-centric research. To explore the potential of our proposed MVHumanNet++ dataset in various 2D and 3D visual tasks, we conducted several pilot studies to demonstrate the performance improvements and effective applications enabled by the scale provided by MVHumanNet++. As the current largest-scale 3D human dataset, we hope that the release of MVHumanNet++ dataset with annotations will foster further innovations in the domain of 3D human-centric tasks at scale. MVHumanNet++ is publicly available at https://kevinlee09.github.io/research/MVHumanNet++/.