imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose
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Abstract (arXiv)
We present imGHUM, the first holistic generative model of 3D human shape and articulated pose, represented as a signed distance function. In contrast to prior work, we model the full human body implicitly as a function zero-level-set and without the use of an explicit template mesh. We propose a novel network architecture and a learning paradigm, which make it possible to learn a detailed implicit generative model of human pose, shape, and semantics, on par with state-of-the-art mesh-based models. Our model features desired detail for human models, such as articulated pose including hand motion and facial expressions, a broad spectrum of shape variations, and can be queried at arbitrary resolutions and spatial locations. Additionally, our model has attached spatial semantics making it straightforward to establish correspondences between different shape instances, thus enabling applications that are difficult to tackle using classical implicit representations. In extensive experiments, we demonstrate the model accuracy and its applicability to current research problems.
Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). Builds on 36 of its 56 references that are in the map. First posted on arXiv 24 Aug 2021.
Builds on
36 of 56 references.
References · 36
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24 more
- 2020Convolutional Occupancy Networkscited by 760
- 2020Implicit Geometric Regularization for Learning Shapescited by 707
- 2020PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitizationcited by 521
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- 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
- 2020Neural Unsigned Distance Fields for Implicit Function Learningcited by 265
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- 2019Learning Shape Templates with Structured Implicit Functionscited by 242
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- 2020STAR: Sparse Trained Articulated Human Body Regressorcited by 194
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- 2020Grasping Field: Learning Implicit Representations for Human Graspscited by 178
- 2019Learning to Reconstruct People in Clothing from a Single RGB Cameracited by 168
- 2020LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registrationcited by 108
- 2021LEAP: Learning Articulated Occupancy of Peoplecited by 107
- 2020Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flowscited by 94
- 2019Deep Level Sets: Implicit Surface Representations for 3D Shape Inferencecited by 52
- 2015Building Statistical Shape Spaces for 3D Human Modelingcited by 47
- 2020Implicit Feature Networks for Texture Completion from Partial 3D Datacited by 31
- 2020TexMesh: Reconstructing Detailed Human Texture and Geometry from RGB-D Videocited by 27
- 2020Ladybird: Quasi-Monte Carlo Sampling for Deep Implicit Field Based 3D Reconstruction with Symmetrycited by 24
- 2018Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequencescited by 19
- 2019Human Synthesis and Scene Compositingcited by 11
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Citing papers per year: 2021: 7; 2022: 25; 2023: 29; 2024: 21; 2025: 13; 2026: 7.
102 papers.
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