用合成数据与解耦学习，从图像中精准估计马的3D形态与姿态。
Dessie: Disentanglement for Articulated 3D Horse Shape and Pose Estimation from Images
- 通过文本生成纹理和合成数据流，构建可解耦的马体形态与姿态空间。
- 在真实图像上重建精度超越现有方法，对斑马、牛等动物也有效。
- 适合做动物3D重建、计算机视觉中形姿解耦研究的开发者参考。
近年来，3D参数化动物模型被用于从图像和视频中估计3D形态与姿态。尽管人类相关研究进展显著，但动物因标注数据有限而更具挑战性。为此，我们提出首个结合合成数据生成与解耦学习的方法，实现3D形态与姿态的回归。聚焦马匹，我们利用文本驱动的纹理生成与合成数据管道，创建多样化的形态、姿态与外观，学习解耦表征空间。所提方法Dessie在3D马体重建任务中优于现有方法，并可泛化至斑马、牛、鹿等大型动物。项目主页：https://celiali.github.io/Dessie/
原文摘要 · Abstract (English)
In recent years, 3D parametric animal models have been developed to aid in estimating 3D shape and pose from images and video. While progress has been made for humans, it's more challenging for animals due to limited annotated data. To address this, we introduce the first method using synthetic data generation and disentanglement to learn to regress 3D shape and pose. Focusing on horses, we use text-based texture generation and a synthetic data pipeline to create varied shapes, poses, and appearances, learning disentangled spaces. Our method, Dessie, surpasses existing 3D horse reconstruction methods and generalizes to other large animals like zebras, cows, and deer. See the project website at: \url{https://celiali.github.io/Dessie/}.
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