LEAPSE: Learning Environment Affordances for 3D Human Pose and Shape Estimation.

Area of Science:

• Computer Vision
• Human Pose Estimation
• Machine Learning

Background:

• Human interaction in 3D environments necessitates understanding posed humans and their surroundings.
• Current 3D human pose estimation methods often neglect environmental context, leading to incomplete estimations.
• Perceiving and interpreting environmental interactions is crucial for accurate 3D human pose understanding.

Purpose of the Study:

• To propose LEAPSE (Learning Environment Affordances for Posed body Scene Estimation), a novel non-parametric method for estimating posed 3D human bodies from single RGB images.
• To capture and represent environmental influences, termed "environment affordances", on human pose estimation.
• To improve the accuracy and robustness of 3D human pose estimation by integrating environmental context.

Main Methods:

• LEAPSE learns salient instance affordances by considering both non-contact and contact instances in the environment.
• The method models global relationships between 3D joints, body mesh vertices, and salient environmental instances.
• A non-parametric approach is utilized for learning environment affordances directly from single RGB images.

Main Results:

• LEAPSE achieved state-of-the-art results on the 3DPW dataset, particularly with numerous affordance instances.
• The method demonstrated excellent performance on the Human3.6M dataset.
• Integrating LEAPSE's environment affordance module significantly enhanced the performance of existing pose estimation models.

Conclusions:

• LEAPSE offers a novel and effective approach to 3D human pose estimation by incorporating environmental context.
• The concept of "environment affordances" provides a valuable representation for improving human-body scene understanding.
• LEAPSE demonstrates the potential to significantly advance the field of 3D human pose estimation from single images.