Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose
  Inverse Kinematics
In 3D human pose and shape estimation, SMPLify remains a robust baseline that
solves inverse kinematics (IK) through iterative optimization. However, its
high computational cost limits its practicality. Recent advances across domains
have shown that replacing iterative optimization with data-driven neural
networks can achieve significant runtime improvements without sacrificing
accuracy. Motivated by this trend, we propose Learnable SMPLify, a neural
framework that replaces the iterative fitting process in SMPLify with a
single-pass regression model. The design of our framework targets two core
challenges in neural IK: data construction and generalization. To enable
effective training, we propose a temporal sampling strategy that constructs
initialization-target pairs from sequential frames. To improve generalization
across diverse motions and unseen poses, we propose a human-centric
normalization scheme and residual learning to narrow the solution space.
Learnable SMPLify supports both sequential inference and plug-in
post-processing to refine existing image-based estimators. Extensive
experiments demonstrate that our method establishes itself as a practical and
simple baseline: it achieves nearly 200x faster runtime compared to SMPLify,
generalizes well to unseen 3DPW and RICH, and operates in a model-agnostic
manner when used as a plug-in tool on LucidAction. The code is available at
https://github.com/Charrrrrlie/Learnable-SMPLify.
68abf69c86b21a0e2e358b4d
Yuchen Yang
claimed_verified
2025-08-25T08:18:42.071Z
670f667d7d1b0373c63f1620
/avatars/21297c0b00cad7ea37c9167d113e2586.svg
Yuchen Yang
user
Charlie019
68abf69c86b21a0e2e358b4e
Linfeng Dong
68abf69c86b21a0e2e358b4f
Wei Wang
68abf69c86b21a0e2e358b50
Zhihang Zhong
68abf69c86b21a0e2e358b51
Xiao Sun
Learnable SMPLify: A Neural Solution for Optimization-Free Human Pose
  Inverse Kinematics
In 3D human pose and shape estimation, SMPLify remains a robust baseline that
solves inverse kinematics (IK) through iterative optimization. However, its
high computational cost limits its practicality. Recent advances across domains
have shown that replacing iterative optimization with data-driven neural
networks can achieve significant runtime improvements without sacrificing
accuracy. Motivated by this trend, we propose Learnable SMPLify, a neural
framework that replaces the iterative fitting process in SMPLify with a
single-pass regression model. The design of our framework targets two core
challenges in neural IK: data construction and generalization. To enable
effective training, we propose a temporal sampling strategy that constructs
initialization-target pairs from sequential frames. To improve generalization
across diverse motions and unseen poses, we propose a human-centric
normalization scheme and residual learning to narrow the solution space.
Learnable SMPLify supports both sequential inference and plug-in
post-processing to refine existing image-based estimators. Extensive
experiments demonstrate that our method establishes itself as a practical and
simple baseline: it achieves nearly 200x faster runtime compared to SMPLify,
generalizes well to unseen 3DPW and RICH, and operates in a model-agnostic
manner when used as a plug-in tool on LucidAction. The code is available at
https://github.com/Charrrrrlie/Learnable-SMPLify.
Learnable SMPLify replaces iterative optimization in SMPLify with a neural network for faster and more generalized 3D human pose and shape estimation.
SMPLify inverse kinematics iterative optimization neural networks single-pass regression model temporal sampling human-centric normalization residual learning sequential inference plug-in post-processing 3DPW RICH LucidAction