# PRIMA: Boosting Animal Mesh Recovery with Biological Priors and Test-Time Adaptation

arXiv: https://arxiv.org/abs/2606.02366 · HTML: https://mlsanity.com/paper/2606.02366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 57 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 30 of 49)

- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image](https://mlsanity.com/paper/1607.08128/) (2016)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [Humans in 4D: Reconstructing and Tracking Humans with Transformers](https://mlsanity.com/paper/2305.20091/) (2023)
- [HuMoR: 3D Human Motion Model for Robust Pose Estimation](https://mlsanity.com/paper/2105.04668/) (2021)
- [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)
- [Reconstructing Hands in 3D with Transformers](https://mlsanity.com/paper/2312.05251/) (2023)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation](https://mlsanity.com/paper/2004.03686/) (2020)
- [Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"](https://mlsanity.com/paper/1908.07201/) (2019)
- [TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation](https://mlsanity.com/paper/2404.16752/) (2024)
- [Creatures great and SMAL: Recovering the shape and motion of animals from video](https://mlsanity.com/paper/1811.05804/) (2018)
- [Articulation-aware Canonical Surface Mapping](https://mlsanity.com/paper/2004.00614/) (2020)
- [Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop](https://mlsanity.com/paper/2007.11110/) (2020)
- [SAM 3D Body: Robust Full-Body Human Mesh Recovery](https://mlsanity.com/paper/2602.15989/) (2026)
- [LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery](https://mlsanity.com/paper/2207.03434/) (2022)
- [Learning from Synthetic Animals](https://mlsanity.com/paper/1912.08265/) (2019)
- [Learning the 3D Fauna of the Web](https://mlsanity.com/paper/2401.02400/) (2024)
- [BARC: Learning to Regress 3D Dog Shape from Images by Exploiting Breed Information](https://mlsanity.com/paper/2203.15536/) (2022)
- [BioCLIP 2: Emergent Properties from Scaling Hierarchical Contrastive Learning](https://mlsanity.com/paper/2505.23883/) (2025)
- [Reconstructing 3D Human Pose by Watching Humans in the Mirror](https://mlsanity.com/paper/2104.00340/) (2021)
- [Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape](https://mlsanity.com/paper/2308.11737/) (2023)
- [3D Bird Reconstruction: a Dataset, Model, and Shape Recovery from a Single View](https://mlsanity.com/paper/2008.06133/) (2020)
- [SuperAnimal pretrained pose estimation models for behavioral analysis](https://mlsanity.com/paper/2203.07436/) (2022)
- [Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation](https://mlsanity.com/paper/2007.02054/) (2020)
- [Hi-LASSIE: High-Fidelity Articulated Shape and Skeleton Discovery from Sparse Image Ensemble](https://mlsanity.com/paper/2212.11042/) (2022)

## Built on by (most cited first, 1 of 1)

- [ORMA: Optimization-based Monocular 4D Reconstruction of Articulated Animals](https://mlsanity.com/paper/2609.37986/) (2026)

## Abstract (arXiv)

We present PRIMA (*PRI*ors for *M*esh *A*daptation), a framework for robust 3D quadruped mesh recovery under severe species and pose imbalance. Existing animal reconstruction methods often regress toward mean shapes and poses due to limited 3D supervision and long-tailed species distributions, resulting in poor generalization to underrepresented animals and rare articulations. PRIMA addresses this challenge through three key contributions. First, we incorporate BioCLIP embeddings as biological priors to inject semantic and morphological knowledge into the reconstruction process, enabling more accurate and generalizable shape prediction across diverse quadrupeds. Second, we introduce a test-time adaptation (TTA) strategy that refines SMAL predictions using 2D reprojection constraints together with auxiliary keypoint guidance, improving pose and shape estimation while enabling the generation of high-quality pseudo-3D annotations from existing 2D datasets. Third, leveraging this TTA framework, we construct Quadruped3D, a large-scale pseudo-3D dataset that covers diverse species and pose variations to systematically improve model performance. Extensive experiments on Animal3D, CtrlAni3D, Quadruped2D, and Animal Kingdom demonstrate that PRIMA achieves state-of-the-art results, with particularly strong improvements on underrepresented species and challenging poses. Our results highlight the importance of biological priors and adaptation-driven data expansion for scalable and generalizable animal mesh recovery. Code is available at https://github.com/AdaptiveMotorControlLab/PRIMA.