8 citations · 19 across the 12 of their papers we have counts for
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cs.CV2023
Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation
Shentong Mo, Enze Xie, Yue Wu +3
Diffusion Transformers have recently shown remarkable effectiveness in generating high-quality 3D point clouds. However, training voxel-based diffusion models for high-resolution 3…
cs.CV2023★ 3 cited
Drag-A-Video: Non-rigid Video Editing with Point-based Interaction
Yao Teng, Enze Xie, Yue Wu +3
Video editing is a challenging task that requires manipulating videos on both the spatial and temporal dimensions. Existing methods for video editing mainly focus on changing the a…
cs.CV2023★ 8 cited
PixArt-: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
Junsong Chen, Jincheng Yu, Chongjian Ge +8
The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community…