4D-Animal: Freely Reconstructing Animatable 3D Animals from Videos
Shanshan Zhong, Jiawei Peng, Zehan Zheng, Zhongzhan Huang, Wufei Ma, Guofeng Zhang, Qihao Liu, Alan Yuille, Jieneng Chen

TL;DR
4D-Animal introduces a keypoint-free framework for reconstructing animatable 3D animals from videos, utilizing dense features and hierarchical alignment to improve efficiency, stability, and temporal coherence.
Contribution
It presents a novel keypoint-free approach with dense feature mapping and multi-level alignment strategies for 3D animal reconstruction from videos.
Findings
Outperforms existing model-based and model-free methods.
Produces high-quality, temporally coherent 3D animal reconstructions.
Enables large-scale applications with improved stability and efficiency.
Abstract
Existing methods for reconstructing animatable 3D animals from videos typically rely on sparse semantic keypoints to fit parametric models. However, obtaining such keypoints is labor-intensive, and keypoint detectors trained on limited animal data are often unreliable. To address this, we propose 4D-Animal, a novel framework that reconstructs animatable 3D animals from videos without requiring sparse keypoint annotations. Our approach introduces a dense feature network that maps 2D representations to SMAL parameters, enhancing both the efficiency and stability of the fitting process. Furthermore, we develop a hierarchical alignment strategy that integrates silhouette, part-level, pixel-level, and temporal cues from pre-trained 2D visual models to produce accurate and temporally coherent reconstructions across frames. Extensive experiments demonstrate that 4D-Animal outperforms both…
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Taxonomy
TopicsImage Processing and 3D Reconstruction · Generative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques
