How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects

• URL: http://arxiv.org/abs/2503.04257v1
• Date: Thu, 06 Mar 2025 09:39:09 GMT
• Title: How to Move Your Dragon: Text-to-Motion Synthesis for Large-Vocabulary Objects
• Authors: Wonkwang Lee, Jongwon Jeong, Taehong Moon, Hyeon-Jong Kim, Jaehyeon Kim, Gunhee Kim, Byeong-Uk Lee,
• Abstract summary: Motion synthesis for diverse object categories holds great potential for 3D content creation.<n>We present a method to generate high-fidelity motions from textual descriptions for diverse and even unseen objects.<n> Experiments show that our method learns to generate high-fidelity motions from textual descriptions for diverse and even unseen objects.
• Score: 37.10752536568922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motion synthesis for diverse object categories holds great potential for 3D content creation but remains underexplored due to two key challenges: (1) the lack of comprehensive motion datasets that include a wide range of high-quality motions and annotations, and (2) the absence of methods capable of handling heterogeneous skeletal templates from diverse objects. To address these challenges, we contribute the following: First, we augment the Truebones Zoo dataset, a high-quality animal motion dataset covering over 70 species, by annotating it with detailed text descriptions, making it suitable for text-based motion synthesis. Second, we introduce rig augmentation techniques that generate diverse motion data while preserving consistent dynamics, enabling models to adapt to various skeletal configurations. Finally, we redesign existing motion diffusion models to dynamically adapt to arbitrary skeletal templates, enabling motion synthesis for a diverse range of objects with varying structures. Experiments show that our method learns to generate high-fidelity motions from textual descriptions for diverse and even unseen objects, setting a strong foundation for motion synthesis across diverse object categories and skeletal templates. Qualitative results are available on this link: t2m4lvo.github.io

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