  Recovering whole-body mesh by inferring the abstract pose and shape
parameters from visual content can obtain 3D bodies with realistic structures.
However, the inferring process is highly non-linear and suffers from image-mesh
misalignment, resulting in inaccurate reconstruction. In contrast, 3D keypoint
estimation methods utilize the volumetric representation to achieve pixel-level
accuracy but may predict unrealistic body structures. To address these issues,
this paper presents a novel hybrid inverse kinematics solution, HybrIK, that
integrates the merits of 3D keypoint estimation and body mesh recovery in a
unified framework. HybrIK directly transforms accurate 3D joints to body-part
rotations via twist-and-swing decomposition. The swing rotations are
analytically solved with 3D joints, while the twist rotations are derived from
visual cues through neural networks. To capture comprehensive whole-body
details, we further develop a holistic framework, HybrIK-X, which enhances
HybrIK with articulated hands and an expressive face. HybrIK-X is fast and
accurate by solving the whole-body pose with a one-stage model. Experiments
demonstrate that HybrIK and HybrIK-X preserve both the accuracy of 3D joints
and the realistic structure of the parametric human model, leading to
pixel-aligned whole-body mesh recovery. The proposed method significantly
surpasses the state-of-the-art methods on various benchmarks for body-only,
hand-only, and whole-body scenarios. Code and results can be found at
https://jeffli.site/HybrIK-X/
