4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans
arXiv:2607.27634v1 Announce Type: new Abstract: Generating high-quality 360-degree dynamic human assets from text prompts is challenging. Existing methods usually synthesize monocular or multi-view videos first and then fit a 4D representation, which is expensive and often causes incomplete geometry or view-inconsistent renderings. We present 4DHumanDiff, a diffusion framework that directly generates dynamic humans represented by 4D Gaussian Splatting (4DGS) from text prompts. By modeling the st...
arXiv cs.CV
·Renlong Wu, Haoran Chen, Yuxiang Wei, Xiaowei Jin, Wangmeng Zuo, Hui Li
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