4 citations · 9 across the 7 of their papers we have counts for
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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.CV2023★ 4 cited
HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting
Xian Liu, Xiaohang Zhan, Jiaxiang Tang +5
Realistic 3D human generation from text prompts is a desirable yet challenging task. Existing methods optimize 3D representations like mesh or neural fields via score distillation…
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…