Semantics-aware Motion Retargeting with Vision-Language Models

• URL: http://arxiv.org/abs/2312.01964v3
• Date: Mon, 15 Apr 2024 15:00:49 GMT
• Title: Semantics-aware Motion Retargeting with Vision-Language Models
• Authors: Haodong Zhang, ZhiKe Chen, Haocheng Xu, Lei Hao, Xiaofei Wu, Songcen Xu, Zhensong Zhang, Yue Wang, Rong Xiong,
• Abstract summary: We present a novel Semantics-aware Motion reTargeting (SMT) method with the advantage of vision-language models to extract and maintain meaningful motion semantics. We utilize a differentiable module to render 3D motions and the high-level motion semantics are incorporated into the motion process by feeding the vision-language model and aligning the extracted semantic embeddings. To ensure the preservation of fine-grained motion details and high-level semantics, we adopt two-stage pipeline consisting of skeleton-aware pre-training and fine-tuning with semantics and geometry constraints.
• Score: 19.53696208117539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Capturing and preserving motion semantics is essential to motion retargeting between animation characters. However, most of the previous works neglect the semantic information or rely on human-designed joint-level representations. Here, we present a novel Semantics-aware Motion reTargeting (SMT) method with the advantage of vision-language models to extract and maintain meaningful motion semantics. We utilize a differentiable module to render 3D motions. Then the high-level motion semantics are incorporated into the motion retargeting process by feeding the vision-language model with the rendered images and aligning the extracted semantic embeddings. To ensure the preservation of fine-grained motion details and high-level semantics, we adopt a two-stage pipeline consisting of skeleton-aware pre-training and fine-tuning with semantics and geometry constraints. Experimental results show the effectiveness of the proposed method in producing high-quality motion retargeting results while accurately preserving motion semantics.

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