# Learning 3D Deformation of Animals from 2D Images

arXiv: https://arxiv.org/abs/1507.07646 · HTML: https://mlsanity.com/paper/1507.07646/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2015.

## Built on by (most cited first, 11 of 11)

- [A-SDF: Learning Disentangled Signed Distance Functions for Articulated Shape Representation](https://mlsanity.com/paper/2104.07645/) (2021)
- [From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/2103.14843/) (2021)
- [Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes](https://mlsanity.com/paper/2205.02904/) (2022)
- [Generative Zoo](https://mlsanity.com/paper/2412.08101/) (2024)
- [Artemis: Articulated Neural Pets with Appearance and Motion synthesis](https://mlsanity.com/paper/2202.05628/) (2022)
- [Reconstructing Animals and the Wild](https://mlsanity.com/paper/2411.18807/) (2024)
- [NeuralFur: Animal Fur Reconstruction From Multi-View Images](https://mlsanity.com/paper/2601.12481/) (2026)
- [Toon3D: Seeing Cartoons from New Perspectives](https://mlsanity.com/paper/2405.10320/) (2024)
- [Advances and Trends in the 3D Reconstruction of the Shape and Motion of Animals](https://mlsanity.com/paper/2508.16062/) (2025)
- [Of Mice and Pose: 2D Mouse Pose Estimation from Unlabelled Data and Synthetic Prior](https://mlsanity.com/paper/2307.13361/) (2023)
- [3D shape reconstruction of semi-transparent worms](https://mlsanity.com/paper/2304.14841/) (2023)

## Abstract (arXiv)

Understanding how an animal can deform and articulate is essential for a realistic modification of its 3D model. In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal. We present a volumetric deformation framework that produces a set of new 3D models by deforming a template 3D model according to a set of user-clicked images. Our framework is based on a novel locally-bounded deformation energy, where every local region has its own stiffness value that bounds how much distortion is allowed at that location. We jointly learn the local stiffness bounds as we deform the template 3D mesh to match each user-clicked image. We show that this seemingly complex task can be solved as a sequence of convex optimization problems. We demonstrate the effectiveness of our approach on cats and horses, which are highly deformable and articulated animals. Our framework produces new 3D models of animals that are significantly more plausible than methods without learned stiffness.