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NASA: Neural Articulated Shape Approximation

arXiv 1912.03207 ↗First posted 6 Dec 2019cs.CV cs.GR cs.LGcited by 149

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Abstract (arXiv)

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. This paper introduces neural articulated shape approximation (NASA), an alternative framework that enables efficient representation of articulated deformable objects using neural indicator functions that are conditioned on pose. Occupancy testing using NASA is straightforward, circumventing the complexity of meshes and the issue of water-tightness. We demonstrate the effectiveness of NASA for 3D tracking applications, and discuss other potential extensions.

Key facts. Cited by 149 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Dec 2019.

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Citing papers per year: 2021: 38; 2022: 44; 2023: 38; 2024: 15; 2025: 7; 2026: 7.

149 papers.

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  1. 2021Neural Fields in Visual Computing and Beyondcited by 539
  2. 2022Decomposing NeRF for Editing via Feature Field Distillationcited by 370
  3. 2021Animatable Neural Radiance Fields for Modeling Dynamic Human Bodiescited by 361
  4. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  5. 2021A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Posecited by 233
  6. 2021I M Avatar: Implicit Morphable Head Avatars from Videoscited by 230
  7. 2021Neural Articulated Radiance Fieldcited by 196
  8. 2021SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networkscited by 181
  9. 2022TAVA: Template-free Animatable Volumetric Actorscited by 172
  10. 2023GauHuman: Articulated Gaussian Splatting from Monocular Human Videoscited by 168
  11. 2022Ditto: Building Digital Twins of Articulated Objects from Interactioncited by 152
  12. 2021GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic Camerascited by 151
38 more
  1. 2022SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Videocited by 144
  2. 2021Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPscited by 134
  3. 2021How to represent part-whole hierarchies in a neural networkcited by 123
  4. 2023Drivable 3D Gaussian Avatarscited by 120
  5. 2021NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenescited by 119
  6. 2022Structured Local Radiance Fields for Human Avatar Modelingcited by 116
  7. 2023PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objectscited by 115
  8. 2021A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representationcited by 113
  9. 2022Recovering 3D Human Mesh from Monocular Images: A Surveycited by 103
  10. 2021Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothingcited by 102
  11. 2023Neural Kernel Surface Reconstructioncited by 102
  12. 2022VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolutioncited by 101
  13. 2022POCO: Point Convolution for Surface Reconstructioncited by 91
  14. 2023Hi4D: 4D Instance Segmentation of Close Human Interactioncited by 87
  15. 2022SNUG: Self-Supervised Neural Dynamic Garmentscited by 83
  16. 2022Transformers as Meta-Learners for Implicit Neural Representationscited by 79
  17. 2021MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Imagescited by 78
  18. 2021MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Imagecited by 74
  19. 2023Learning Neural Volumetric Representations of Dynamic Humans in Minutescited by 70
  20. 2021DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Camerascited by 67
  21. 2021CoNeRF: Controllable Neural Radiance Fieldscited by 65
  22. 2022Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representationscited by 65
  23. 2022LISA: Learning Implicit Shape and Appearance of Handscited by 63
  24. 2021CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assemblycited by 62
  25. 2021Geometry-Consistent Neural Shape Representation with Implicit Displacement Fieldscited by 60
  26. 2022COAP: Compositional Articulated Occupancy of Peoplecited by 59
  27. 2021Neural Fields as Learnable Kernels for 3D Reconstructioncited by 58
  28. 2022AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstructioncited by 58
  29. 2022CaDeX: Learning Canonical Deformation Coordinate Space for Dynamic Surface Representation via Neural Homeomorphismcited by 56
  30. 2021Implicit Neural Video Compressioncited by 52
  31. 2023ContactGen: Generative Contact Modeling for Grasp Generationcited by 52
  32. 2022Self-supervised Neural Articulated Shape and Appearance Modelscited by 51
  33. 2021Seeing Implicit Neural Representations as Fourier Seriescited by 50
  34. 2021Watch It Move: Unsupervised Discovery of 3D Joints for Re-Posing of Articulated Objectscited by 50
  35. 2022Learning Implicit Templates for Point-Based Clothed Human Modelingcited by 50
  36. 2023PoseVocab: Learning Joint-structured Pose Embeddings for Human Avatar Modelingcited by 50
  37. 2022PINA: Learning a Personalized Implicit Neural Avatar from a Single RGB-D Video Sequencecited by 46
  38. 2024REACTO: Reconstructing Articulated Objects from a Single Videocited by 45

50 most cited of 149 All 149 (JSON)