Toward Democratizing Human Motion Generation
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Date
2025-04-24
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Tel Aviv University
Abstract
Human motion generation is a challenging task due to the intricate complexity of human movement. Capturing the subtle dynamics of coordination, balance, and expression requires models capable of synthesizing both the physical plausibility and the nuanced variability inherent in human motion. Furthermore, the interdependence of spatial and temporal factors makes designing effective algorithms an intricate problem. As a result, motion generation remains accessible primarily to professional users, and even for them, it is a labor-intensive process requiring significant expertise and resources. The overarching goal of this work is to develop generative tools and intuitive controls that empower content creators, democratizing human motion synthesis and addressing these challenges. By leveraging advances in machine learning and generative Artificial intelligence (AI), this research seeks to enable users, regardless of expertise, to produce realistic, diverse, and context-aware animations with minimal effort. Such tools are not only intended to ease the technical and creative burden for professionals but also to open up animation and motion creation to a broader audience, making the process approachable, efficient, and cost-effective. The journey begins with MotionCLIP, which bridges the human motion domain with the semantic richness of CLIP. By aligning human motion representations with CLIP’s text and image embeddings, MotionCLIP enables text-to-motion generation, semantic editing, and interpolation. Its capability to interpret abstract prompts is exemplified by its ability to generate a sitting motion from the prompt "couch" or mimic a web-swinging motion from "Spiderman". These results demonstrate MotionCLIP’s potential to create nuanced animations and expand the creative toolkit for animators and novices alike. Next, the Motion Diffusion Model (MDM) introduces diffusion processes into motion synthesis, addressing the diversity and many-to-many mapping inherent in human motion. MDM combines a lightweight transformer architecture with geometric losses to ensure physically plausible and visually coherent results. It excels in tasks like text-to-motion and action-to-motion, offering state-of-the-art performance on benchmarks while requiring modest computational resources. MDM’s versatility is further demonstrated in inpainting tasks, such as filling gaps in motion sequences or editing specific body parts while preserving the rest of the animation. Extending the utility of diffusion models, Human Motion Diffusion as a Generative Prior explores advanced composition techniques for motion generation via different types of composition. Sequential composition enables the synthesis of long, coherent animations by stitching shorter segments, while parallel composition allows the generation of multi-character interactions using a lightweight communication block. Model composition offers fine-grained control, blending priors to edit and refine joint-level motion trajectories. These methods highlight how generative priors can support complex and nuanced motion applications, addressing previously unmet needs in the field. Finally, we suggest integrating data-driven motion generation into physics simulation through CLoSD, a framework that combines motion diffusion models with reinforcement learning (RL). Acting as a universal planner, the diffusion module generates text-driven motion plans, while the RL controller ensures physical plausibility and interaction with the environment. This synergy enables characters to perform a variety of tasks, from navigating to a goal to object interactions and transitioning between actions like sitting and standing. CLoSD thus bridges the gap between intuitive control and physical realism, opening new horizons for interactive motion generation. By addressing the inherent challenges of motion synthesis using neural generative methods, this work influenced how motion is created and controlled. Its contributions lay a groundwork for intuitive, democratized tools that will potentially empower professionals and novices to produce rich, realistic animations.
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