Motion Generation: A Survey of Generative Approaches and Benchmarks

2025-07-07

AI Summary

The field of motion generation lacks a systematic survey grounded in generative methodology. To address this gap, we propose the first deep taxonomy centered on generative strategies, synthesizing state-of-the-art works from top-tier conferences (CVPR, ICCV, SIGGRAPH, CoRL) since 2023. Our framework uniformly analyzes four dominant paradigms—GANs, autoencoders, autoregressive models, and diffusion models—across three dimensions: architectural design, conditional modeling mechanisms, and evaluation protocols for motion sequence synthesis. We consolidate widely adopted datasets and metrics, identifying key challenges including motion coherence, physical plausibility, and cross-domain generalization. By establishing a comprehensive, comparable analytical benchmark with well-defined dimensions, our survey significantly enhances methodological comparability and facilitates precise problem diagnosis. This work serves as a foundational reference for researchers advancing generative motion modeling.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Motion & Tracking
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Motion generation, the task of synthesizing realistic motion sequences from various conditioning inputs, has become a central problem in computer vision, computer graphics, and robotics, with applications ranging from animation and virtual agents to human-robot interaction. As the field has rapidly progressed with the introduction of diverse modeling paradigms including GANs, autoencoders, autoregressive models, and diffusion-based techniques, each approach brings its own advantages and limitations. This growing diversity has created a need for a comprehensive and structured review that specifically examines recent developments from the perspective of the generative approach employed. In this survey, we provide an in-depth categorization of motion generation methods based on their underlying generative strategies. Our main focus is on papers published in top-tier venues since 2023, reflecting the most recent advancements in the field. In addition, we analyze architectural principles, conditioning mechanisms, and generation settings, and compile a detailed overview of the evaluation metrics and datasets used across the literature. Our objective is to enable clearer comparisons and identify open challenges, thereby offering a timely and foundational reference for researchers and practitioners navigating the rapidly evolving landscape of motion generation.

Problem

• Survey generative approaches for realistic motion sequence synthesis
• Compare advantages and limitations of diverse modeling paradigms
• Provide structured review of recent motion generation advancements

Innovation

• Survey categorizes motion generation methods
• Focuses on generative strategies since 2023
• Analyzes architectures, metrics, and datasets