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Motion Generation: A Survey of Generative Approaches and Benchmarks
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Motion Generation: A Survey of Generative Approaches and Benchmarks
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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.
Forward citations
Cited by 5 Pith papers
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IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation
Interleaving motion generation with text-motion assessment and refinement improves alignment between generated human motion and goal text.
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FunPhase: A Periodic Functional Autoencoder for Motion Generation via Phase Manifolds
FunPhase encodes motion clips as sinusoidal phase functions and decodes them continuously in space and time, enabling reconstruction, generation, super-resolution, and body completion across skeletons.
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Social Structure Matters in 3D Human-Human Interaction Generation
Introduces a Solo-to-Social planner-executor framework where LLMs decompose HHI into phases and roles, then a LoRA-adapted solo motion model grounds them into partner-aware 3D motion.
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Coordinate-Based Dual-Constrained Autoregressive Motion Generation
CDAMD is a new autoregressive text-to-motion framework operating on continuous motion coordinates with dual constraints and diffusion-inspired components, establishing new benchmarks and claiming SOTA fidelity plus se...
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Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation
Proposes LoRA-based mixture-of-experts with autoencoder routing for continual bidirectional motion-language learning, reporting near-zero forgetting on a 5-task HumanML3D benchmark derived via semantic clustering.
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