HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation (2011.14672v4)

Abstract: Model-based 3D pose and shape estimation methods reconstruct a full 3D mesh for the human body by estimating several parameters. However, learning the abstract parameters is a highly non-linear process and suffers from image-model misalignment, leading to mediocre model performance. In contrast, 3D keypoint estimation methods combine deep CNN network with the volumetric representation to achieve pixel-level localization accuracy but may predict unrealistic body structure. In this paper, we address the above issues by bridging the gap between body mesh estimation and 3D keypoint estimation. We propose a novel hybrid inverse kinematics solution (HybrIK). HybrIK directly transforms accurate 3D joints to relative body-part rotations for 3D body mesh reconstruction, via the twist-and-swing decomposition. The swing rotation is analytically solved with 3D joints, and the twist rotation is derived from the visual cues through the neural network. We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods. Without bells and whistles, the proposed method surpasses the state-of-the-art methods by a large margin on various 3D human pose and shape benchmarks. As an illustrative example, HybrIK outperforms all the previous methods by 13.2 mm MPJPE and 21.9 mm PVE on 3DPW dataset. Our code is available at https://github.com/Jeff-sjtu/HybrIK.

• The paper introduces HybrIK, which combines analytical twist‐and‐swing decomposition with neural twist estimation for precise 3D human mesh reconstruction.
• It aligns 3D keypoint estimates with body-part rotations to achieve improved accuracy and realistic representations over traditional keypoint-only methods.
• Its end-to-end differentiable framework reduces errors by 13.2 mm MPJPE and 21.9 mm PVE on the 3DPW dataset, demonstrating robust real-world performance.

Overview of HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation

The paper "HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation" presents a novel approach to improve 3D human pose and shape estimation by integrating analytical and neural methodologies. This synthesis addresses limitations in model-based techniques and 3D keypoint estimation, achieving superior accuracy and realistic body structure representation.

Key Contributions

1. Hybrid Analytical-Neural Approach: The core contribution is the development of HybrIK, a method that leverages inverse kinematics through twist-and-swing decomposition. Swing rotations, derived analytically from 3D joints, and twist rotations, estimated via a neural network, jointly facilitate accurate 3D body mesh reconstruction.
2. Improved Accuracy and Realism: By aligning 3D joint estimations with body-part rotations, HybrIK ensures both accurate 3D poses and realistic body structures. This method significantly outperforms 3D keypoint estimation alone, achieving refined human body meshes.
3. End-to-End Differentiability: Emphasizing computational efficiency, HybrIK is fully differentiable, enabling seamless training of the 3D joints and human body mesh within an integrated framework.

Numerical Validation

The proposed HybrIK method demonstrates substantial improvements over existing techniques across multiple benchmarks. Notable results include a reduction of the mean per joint position error (MPJPE) by 13.2 mm and per vertex error (PVE) by 21.9 mm on the 3DPW dataset. These findings underscore HybrIK's robustness and effectiveness in realistic scenarios.

Implications and Future Directions

Theoretically, HybrIK bridges a significant gap between traditional model-based and keypoint-driven approaches in human pose estimation, proposing a more integrated solution. Practically, its enhanced accuracy and realism have potential applications in animation, virtual reality, and human-computer interaction.

Looking forward, further exploration into refining twist angle predictions and optimizing shape parameters could unlock additional performance enhancements. As neural networks continue to evolve, integrating more sophisticated learning mechanisms could elevate the precision and applicability of this method even further.

Conclusion

HybrIK signifies a meaningful advancement in 3D human pose and shape estimation by effectively combining analytical insights with neural flexibility. Its proven superiority in balancing accurate representation with computational efficiency sets a new standard, paving the way for future innovations in the field.