3D Human Pose Analysis via Diffusion Synthesis

2024-01-17

arXiv.org

AI Summary

In 3D human pose analysis, existing multi-task approaches—such as pose estimation, completion, and denoising—are typically modeled independently, leading to poor generalization across tasks and scenarios. To address this, we propose PADS, a unified framework based on diffusion models. PADS formulates diverse pose analysis tasks as regularized inverse problems incorporating kinematic constraints, and employs a task-agnostic pose prior to guide conditional denoising—enabling multi-task inference within a single model without fine-tuning. This work is the first to systematically integrate diffusion models into general-purpose 3D pose analysis, establishing an end-to-end “prior learning–inverse problem solving” paradigm. Extensive experiments on multiple benchmarks demonstrate that PADS significantly improves accuracy and robustness under challenging conditions—including occlusion, sensor noise, and missing joints—validating its strong generalization capability and practical applicability.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Diffusion models have demonstrated remarkable success in generative modeling. In this paper, we propose PADS (Pose Analysis by Diffusion Synthesis), a novel framework designed to address various challenges in 3D human pose analysis through a unified pipeline. Central to PADS are two distinctive strategies: i) learning a task-agnostic pose prior using a diffusion synthesis process to effectively capture the kinematic constraints in human pose data, and ii) unifying multiple pose analysis tasks like estimation, completion, denoising, etc, as instances of inverse problems. The learned pose prior will be treated as a regularization imposing on task-specific constraints, guiding the optimization process through a series of conditional denoising steps. PADS represents the first diffusion-based framework for tackling general 3D human pose analysis within the inverse problem framework. Its performance has been validated on different benchmarks, signaling the adaptability and robustness of this pipeline.

Problem

• Unified generative modeling for 3D human pose analysis tasks
• Training-free adaptation to pose estimation, denoising, and completion
• Plug-and-play framework without task-specific supervision or retraining

Innovation

• Plug-and-play framework for 3D pose analysis
• Task-agnostic prior via unconditional diffusion synthesis
• Training-free adaptation via posterior sampling scheme