ArticleCyborg and bionic systems (Washington, D.C.)2024
ASMNet: Action and Style-Conditioned Motion Generative Network for 3D Human Motion Generation.
Article in Cyborg and bionic systems (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Contrastive learning-driven spatiotemporal dynamically adaptive framework for stylized 3D human motion generation.PloS one · 2026Article
- Multi-task deep learning framework combining CNN: vision transformers and PSO for accurate diabetic retinopathy diagnosis and lesion localization.Scientific reports · 2025Article
- Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education.PeerJ. Computer science · 2025Article
- Application of style transfer algorithm in the integration of traditional garden and modern design elements.PloS one · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
Extensive research has explored human motion generation, but the generated sequences are influenced by different motion styles. For instance, the act of walking with joy and sorrow evokes distinct effects on a character's motion. Due to the difficulties in motion capture with styles, the available data for style research are also limited. To address the problems, we propose ASMNet, an action and style-conditioned motion generative network. This network ensures that the generated human motion sequences not only comply with the provided action label but also exhibit distinctive stylistic features. To extract motion features from human motion sequences, we design a spatial temporal extractor. Moreover, we use the adaptive instance normalization layer to inject style into the target motion. Our results are comparable to state-of-the-art approaches and display a substantial advantage in both quantitative and qualitative evaluations. The code is available at https://github.com/ZongYingLi/ASMNet.git.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.