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cs.CV2023
Realistic Human Motion Generation with Cross-Diffusion Models
Zeping Ren, Shaoli Huang, Xiu Li
We introduce the Cross Human Motion Diffusion Model (CrossDiff), a novel approach for generating high-quality human motion based on textual descriptions. Our method integrates 3D a…
cs.CV2023
SemanticBoost: Elevating Motion Generation with Augmented Textual Cues
Xin He, Shaoli Huang, Xiaohang Zhan +2
Current techniques face difficulties in generating motions from intricate semantic descriptions, primarily due to insufficient semantic annotations in datasets and weak contextual…
cs.GR2023★ 1 cited
TapMo: Shape-aware Motion Generation of Skeleton-free Characters
Jiaxu Zhang, Shaoli Huang, Zhigang Tu +4
Previous motion generation methods are limited to the pre-rigged 3D human model, hindering their applications in the animation of various non-rigged characters. In this work, we pr…