Animal 3D Pose

Animal 3D pose estimation aims to reconstruct the three-dimensional posture and shape of animals from visual (and sometimes audio) data, enabling a deeper understanding of animal behavior and movement. Current research focuses on improving accuracy and robustness using various techniques, including incorporating audio cues, leveraging generative models like transformers and VAEs, and employing coarse-to-fine approaches that combine parametric models (e.g., SMAL) with per-vertex deformation refinement. The development of large, high-quality datasets and self-supervised learning methods are crucial for advancing this field, with applications ranging from wildlife conservation to animation and robotics.