Seamless Human Motion Composition with Blended Positional Encodings
Germán Barquero, Sergio Escalera, Cristina Palmero
2024年份
38顶会引用
摘要
forward kick", 2.5s) ("walk slowly", 3.2s) ("get down on ground", 3s) ("crawl", 3.3s) ("walk", 2s) ("walk", 2s) ("walk", 2s) ("walk", 2s) ("walk", 2s) ...
Figure 1. We present FlowMDM, a diffusion-based approach capable of generating seamlessly continuous sequences of human motion from textual descriptions (left). The whole sequence is generated simultaneously and it does not require any postprocessing. FlowMDM also makes strides in the challenging problem of extrapolating and controlling periodic motion such as walking, jumping, or waving (right).
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引用它的顶会 Paper38
- InterDreamer: Zero-Shot Text to 3D Dynamic Human-Object InteractionSirui Xu, Ziyin Wang, Yu-Xiong Wang, Liangyan GuiNeurIPS 2024 · 被引用 78 次
- OpenHOI: Open-World Hand-Object Interaction Synthesis with Multimodal Large Language ModelZhenhao Zhang, Ye Shi, Lingxiao Yang, Suting Ni 等NeurIPS 2025 · 被引用 25 次
- FloodDiffusion: Tailored Diffusion Forcing for Streaming Motion GenerationYIYI CAI, Yuhan Wu, Kunhang Li, YOU ZHOU 等CVPR 2026 · 被引用 14 次
- Sketch2Anim: Towards Transferring Sketch Storyboards into 3D AnimationLei Zhong, Chuan Guo, Yiming Xie, Jiawei Wang 等SIGGRAPH 2025 · 被引用 13 次
- FlashMo: Geometric Interpolants and Frequency-Aware Sparsity for Scalable Efficient Motion GenerationZeyu Zhang, Yiran Wang, Danning Li, Dong Gong 等NeurIPS 2025 · 被引用 12 次
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