NeuMan: Neural Human Radiance Field from a Single Video
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Abstract (arXiv)
Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences. We propose a novel framework to reconstruct the human and the scene that can be rendered with novel human poses and views from just a single in-the-wild video. Given a video captured by a moving camera, we train two NeRF models: a human NeRF model and a scene NeRF model. To train these models, we rely on existing methods to estimate the rough geometry of the human and the scene. Those rough geometry estimates allow us to create a warping field from the observation space to the canonical pose-independent space, where we train the human model in. Our method is able to learn subject specific details, including cloth wrinkles and accessories, from just a 10 seconds video clip, and to provide high quality renderings of the human under novel poses, from novel views, together with the background.
Key facts. Cited by 244 papers in the map (data through 14 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 23 Mar 2022.
Builds on
33 of 54 references.
References · 33
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- 2017Mask R-CNNcited by 7,277
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
- 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,612
- 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
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- 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 642
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- 2019VIBE: Video Inference for Human Body Pose and Shape Estimationcited by 620
- 2018DensePose: Dense Human Pose Estimation In The Wildcited by 575
- 2016MOT16: A Benchmark for Multi-Object Trackingcited by 532
21 more
- 2022HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Videocited by 483
- 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
- 2020End-to-End Human Pose and Mesh Reconstruction with Transformerscited by 418
- 2020Space-time Neural Irradiance Fields for Free-Viewpoint Videocited by 414
- 2021Animatable Neural Radiance Fields for Modeling Dynamic Human Bodiescited by 361
- 2019HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimationcited by 247
- 2021Learning Object-Compositional Neural Radiance Field for Editable Scene Renderingcited by 228
- 2020Monocular, One-stage, Regression of Multiple 3D Peoplecited by 218
- 2019Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesiscited by 161
- 2021Editable Free-viewpoint Video Using a Layered Neural Representationcited by 148
- 2021Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Mergingcited by 104
- 2017Disentangled Person Image Generationcited by 98
- 2018Synthesizing Images of Humans in Unseen Posescited by 97
- 2020Object-Centric Neural Scene Renderingcited by 90
- 2021LOLNeRF: Learn from One Lookcited by 84
- 2018Dense Pose Transfercited by 83
- 2021Neural Re-Rendering of Humans from a Single Imagecited by 59
- 2020Neural Volume Rendering: NeRF And Beyondcited by 44
- 2020Vid2Actor: Free-viewpoint Animatable Person Synthesis from Video in the Wildcited by 35
- 2019Learning Multi-Human Optical Flowcited by 26
- 2021Learning Realistic Human Reposing using Cyclic Self-Supervision with 3D Shape, Pose, and Appearance Consistencycited by 15
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Citing papers per year: 2022: 13; 2023: 80; 2024: 66; 2025: 47; 2026: 38.
244 papers.
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