Motion Generation: A Survey of Generative Approaches and Benchmarks

• URL: http://arxiv.org/abs/2507.05419v1
• Date: Mon, 07 Jul 2025 19:04:56 GMT
• Title: Motion Generation: A Survey of Generative Approaches and Benchmarks
• Authors: Aliasghar Khani, Arianna Rampini, Bruno Roy, Larasika Nadela, Noa Kaplan, Evan Atherton, Derek Cheung, Jacky Bibliowicz,
• Abstract summary: We provide an in-depth categorization of motion generation methods based on their underlying generative strategies.<n>Our main focus is on papers published in top-tier venues since 2023, reflecting the most recent advancements in the field.<n>We analyze architectural principles, conditioning mechanisms, and generation settings, and compile a detailed overview of the evaluation metrics and datasets used across the literature.
• Score: 1.4254358932994455
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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