mlsanityData to 7 Oct 2026

3D Menagerie: Modeling the 3D shape and pose of animals

arXiv 1611.07700 ↗First posted 23 Nov 2016cs.CVcited by 310

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Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we learn our model from a small set of 3D scans of toy figurines in arbitrary poses. We employ a novel part-based shape model to compute an initial registration to the scans. We then normalize their pose, learn a statistical shape model, and refine the registrations and the model together. In this way, we accurately align animal scans from different quadruped families with very different shapes and poses. With the registration to a common template we learn a shape space representing animals including lions, cats, dogs, horses, cows and hippos. Animal shapes can be sampled from the model, posed, animated, and fit to data. We demonstrate generalization by fitting it to images of real animals including species not seen in training.

Key facts. Cited by 310 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Nov 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 41; 2022: 47; 2023: 59; 2024: 59; 2025: 51; 2026: 53.

310 papers.

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  1. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,483
  2. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 555
  3. 2021Neural Articulated Radiance Fieldcited by 196
  4. 2021BANMo: Building Animatable 3D Neural Models from Many Casual Videoscited by 195
  5. 2021Seeing biodiversity: perspectives in machine learning for wildlife conservationcited by 129
  6. 2023Consistent4D: Consistent 360deg Dynamic Object Generation from Monocular Videocited by 124
  7. 20214DComplete: Non-Rigid Motion Estimation Beyond the Observable Surfacecited by 123
  8. 2023GART: Gaussian Articulated Template Modelscited by 121
  9. 2021LASR: Learning Articulated Shape Reconstruction from a Monocular Videocited by 115
  10. 2021A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representationcited by 113
  11. 2022MagicPony: Learning Articulated 3D Animals in the Wildcited by 111
  12. 2022Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understandingcited by 102
38 more
  1. 2022Generative Neural Articulated Radiance Fieldscited by 101
  2. 2021NPMs: Neural Parametric Models for 3D Deformable Shapescited by 89
  3. 2023Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Renderingcited by 84
  4. 20243D Gaussian Splatting: Survey, Technologies, Challenges, and Opportunitiescited by 77
  5. 2025ArtGS: Building Interactable Replicas of Complex Articulated Objects via Gaussian Splattingcited by 67
  6. 2022Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representationscited by 65
  7. 2022LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discoverycited by 65
  8. 2022LISA: Learning Implicit Shape and Appearance of Handscited by 63
  9. 2021DOVE: Learning Deformable 3D Objects by Watching Videoscited by 61
  10. 2022A Survey of Non-Rigid 3D Registrationcited by 59
  11. 20213DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variationscited by 58
  12. 2024Learning the 3D Fauna of the Webcited by 58
  13. 2022Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Cameracited by 57
  14. 2023Unsupervised Learning of Robust Spectral Shape Matchingcited by 57
  15. 2022Fast-SNARF: A Fast Deformer for Articulated Neural Fieldscited by 55
  16. 2023Diffusion 3D Features (Diff3F): Decorating Untextured Shapes with Distilled Semantic Featurescited by 55
  17. 2021Shape registration in the time of transformerscited by 51
  18. 2022BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed Informationcited by 51
  19. 2022Self-supervised Neural Articulated Shape and Appearance Modelscited by 51
  20. 2021From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimationcited by 48
  21. 2023Reconstructing Animatable Categories from Videoscited by 46
  22. 2024REACTO: Reconstructing Articulated Objects from a Single Videocited by 45
  23. 2023Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shapecited by 43
  24. 2023Flow supervision for Deformable NeRFcited by 42
  25. 2022CLIP-Actor: Text-Driven Recommendation and Stylization for Animating Human Meshescited by 41
  26. 2025Puppeteer: Rig and Animate Your 3D Modelscited by 41
  27. 2021ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generatorscited by 40
  28. 2025PhysCtrl: Generative Physics for Controllable and Physics-Grounded Video Generationcited by 39
  29. 2023Dynamic Point Fieldscited by 37
  30. 2025AnyTop: Character Animation Diffusion with Any Topologycited by 37
  31. 2022Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matchingcited by 36
  32. 2022Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matchingcited by 35
  33. 2022CASA: Category-agnostic Skeletal Animal Reconstructioncited by 35
  34. 2023Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Cloudscited by 34
  35. 2023Zero-Shot 3D Shape Correspondencecited by 33
  36. 2024HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splattingcited by 33
  37. 2024DragAPart: Learning a Part-Level Motion Prior for Articulated Objectscited by 32
  38. 2021Birds of a Feather: Capturing Avian Shape Models from Imagescited by 30

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