# BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed Information

arXiv: https://arxiv.org/abs/2203.15536 · HTML: https://mlsanity.com/paper/2203.15536/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 33 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Learning Category-Specific Mesh Reconstruction from Image Collections](https://mlsanity.com/paper/1803.07549/) (2018)
- [3D Menagerie: Modeling the 3D shape and pose of animals](https://mlsanity.com/paper/1611.07700/) (2016)
- [Learning to Estimate 3D Human Pose and Shape from a Single Color Image](https://mlsanity.com/paper/1805.04092/) (2018)
- [Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision](https://mlsanity.com/paper/1905.06817/) (2019)
- [Cross-Domain Adaptation for Animal Pose Estimation](https://mlsanity.com/paper/1908.05806/) (2019)
- [Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"](https://mlsanity.com/paper/1908.07201/) (2019)
- [Efficient Learning on Point Clouds with Basis Point Sets](https://mlsanity.com/paper/1908.09186/) (2019)
- [Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows](https://mlsanity.com/paper/2003.10350/) (2020)
- [Creatures great and SMAL: Recovering the shape and motion of animals from video](https://mlsanity.com/paper/1811.05804/) (2018)
- [Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop](https://mlsanity.com/paper/2007.11110/) (2020)
- [Numerical Coordinate Regression with Convolutional Neural Networks](https://mlsanity.com/paper/1801.07372/) (2018)
- [Learning from Synthetic Animals](https://mlsanity.com/paper/1912.08265/) (2019)
- [DOVE: Learning Deformable 3D Objects by Watching Videos](https://mlsanity.com/paper/2107.10844/) (2021)
- [Implicit Mesh Reconstruction from Unannotated Image Collections](https://mlsanity.com/paper/2007.08504/) (2020)
- [Learning 3D Human Shape and Pose from Dense Body Parts](https://mlsanity.com/paper/1912.13344/) (2019)
- [RGBD-Dog: Predicting Canine Pose from RGBD Sensors](https://mlsanity.com/paper/2004.07788/) (2020)
- [Birds of a Feather: Capturing Avian Shape Models from Images](https://mlsanity.com/paper/2105.09396/) (2021)
- [Transferring Dense Pose to Proximal Animal Classes](https://mlsanity.com/paper/2003.00080/) (2020)

## Built on by (most cited first, 30 of 51)

- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [MotionGPT: Human Motion as a Foreign Language](https://mlsanity.com/paper/2306.14795/) (2023)
- [LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery](https://mlsanity.com/paper/2207.03434/) (2022)
- [Learning the 3D Fauna of the Web](https://mlsanity.com/paper/2401.02400/) (2024)
- [Reconstructing Animatable Categories from Videos](https://mlsanity.com/paper/2305.06351/) (2023)
- [Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape](https://mlsanity.com/paper/2308.11737/) (2023)
- [MotionGPT3: Human Motion as a Second Modality](https://mlsanity.com/paper/2506.24086/) (2025)
- [CASA: Category-agnostic Skeletal Animal Reconstruction](https://mlsanity.com/paper/2211.03568/) (2022)
- [Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble](https://mlsanity.com/paper/2212.11042/) (2022)
- [State of the Art in Dense Monocular Non-Rigid 3D Reconstruction](https://mlsanity.com/paper/2210.15664/) (2022)
- [Neural Shape Deformation Priors](https://mlsanity.com/paper/2210.05616/) (2022)
- [Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos](https://mlsanity.com/paper/2403.17103/) (2024)
- [AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer](https://mlsanity.com/paper/2412.00837/) (2024)
- [SAOR: Single-View Articulated Object Reconstruction](https://mlsanity.com/paper/2303.13514/) (2023)
- [BAGS: Building Animatable Gaussian Splatting from a Monocular Video with Diffusion Priors](https://mlsanity.com/paper/2403.11427/) (2024)
- [Zero-shot Pose Transfer for Unrigged Stylized 3D Characters](https://mlsanity.com/paper/2306.00200/) (2023)
- [Generative Zoo](https://mlsanity.com/paper/2412.08101/) (2024)
- [DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction](https://mlsanity.com/paper/2412.04464/) (2024)
- [Web-Scale Collection of Video Data for 4D Animal Reconstruction](https://mlsanity.com/paper/2511.01169/) (2025)
- [Primitive-based 3D Human-Object Interaction Modelling and Programming](https://mlsanity.com/paper/2312.10714/) (2023)
- [Dessie: Disentanglement for Articulated 3D Horse Shape and Pose Estimation from Images](https://mlsanity.com/paper/2410.03438/) (2024)
- [Reconstructing Animals and the Wild](https://mlsanity.com/paper/2411.18807/) (2024)
- [AniMer+: Unified Pose and Shape Estimation Across Mammalia and Aves via Family-Aware Transformer](https://mlsanity.com/paper/2508.00298/) (2025)
- [SPAC-Net: Synthetic Pose-aware Animal ControlNet for Enhanced Pose Estimation](https://mlsanity.com/paper/2305.17845/) (2023)
- [4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos](https://mlsanity.com/paper/2507.10437/) (2025)
- [Learning Articulated Shape with Keypoint Pseudo-labels from Web Images](https://mlsanity.com/paper/2304.14396/) (2023)
- [Model-based Metric 3D Shape and Motion Reconstruction of Wild Bottlenose Dolphins in Drone-Shot Videos](https://mlsanity.com/paper/2504.15782/) (2025)
- [DogMo: A Large-Scale Multi-View RGB-D Dataset for 4D Canine Motion Recovery](https://mlsanity.com/paper/2510.24117/) (2025)
- [Shape of You: Precise 3D shape estimations for diverse body types](https://mlsanity.com/paper/2304.07389/) (2023)
- [DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose](https://mlsanity.com/paper/2408.14860/) (2024)

## Abstract (arXiv)

Our goal is to recover the 3D shape and pose of dogs from a single image. This is a challenging task because dogs exhibit a wide range of shapes and appearances, and are highly articulated. Recent work has proposed to directly regress the SMAL animal model, with additional limb scale parameters, from images. Our method, called BARC (Breed-Augmented Regression using Classification), goes beyond prior work in several important ways. First, we modify the SMAL shape space to be more appropriate for representing dog shape. But, even with a better shape model, the problem of regressing dog shape from an image is still challenging because we lack paired images with 3D ground truth. To compensate for the lack of paired data, we formulate novel losses that exploit information about dog breeds. In particular, we exploit the fact that dogs of the same breed have similar body shapes. We formulate a novel breed similarity loss consisting of two parts: One term encourages the shape of dogs from the same breed to be more similar than dogs of different breeds. The second one, a breed classification loss, helps to produce recognizable breed-specific shapes. Through ablation studies, we find that our breed losses significantly improve shape accuracy over a baseline without them. We also compare BARC qualitatively to WLDO with a perceptual study and find that our approach produces dogs that are significantly more realistic. This work shows that a-priori information about genetic similarity can help to compensate for the lack of 3D training data. This concept may be applicable to other animal species or groups of species. Our code is publicly available for research purposes at https://barc.is.tue.mpg.de/.