Motion Generation: A Survey of Generative Approaches and Benchmarks

TL;DR

This survey reviews recent advances in motion generation, categorizing methods by their generative strategies, analyzing architectural and conditioning techniques, and summarizing evaluation metrics and datasets to guide future research.

Contribution

It provides a comprehensive, structured overview of recent motion generation methods from 2023 onward, highlighting trends, challenges, and evaluation practices.

Findings

Diverse generative models like GANs, autoencoders, autoregressive, and diffusion techniques are prominent.

Analysis of architectural and conditioning mechanisms reveals key design principles.

Evaluation metrics and datasets vary widely, indicating a need for standardized benchmarks.

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.