BLAPose: Enhancing 3D Human Pose Estimation with Bone Length Adjustment
This work improves 3D human pose estimation for applications like motion capture and robotics by enhancing model accuracy through physical constraints, though it is incremental as it builds on existing models.
The paper tackles the problem of 3D human pose estimation by addressing physical constraints like bone length consistency, introducing a method that predicts bone lengths and adjusts them in models, resulting in significant performance improvements on the Human3.6M dataset.
Current approaches in 3D human pose estimation primarily focus on regressing 3D joint locations, often neglecting critical physical constraints such as bone length consistency and body symmetry. This work introduces a recurrent neural network architecture designed to capture holistic information across entire video sequences, enabling accurate prediction of bone lengths. To enhance training effectiveness, we propose a novel augmentation strategy using synthetic bone lengths that adhere to physical constraints. Moreover, we present a bone length adjustment method that preserves bone orientations while substituting bone lengths with predicted values. Our results demonstrate that existing 3D human pose estimation models can be significantly enhanced through this adjustment process. Furthermore, we fine-tune human pose estimation models using inferred bone lengths, observing notable improvements. Our bone length prediction model surpasses the previous best results, and our adjustment and fine-tuning method enhance performance across several metrics on the Human3.6M dataset.