8 citations · 9 across the 6 of their papers we have counts for
6 papers
GT23D-Bench: A Comprehensive General Text-to-3D Generation Benchmark
Xiao Cai, Sitong Su, Jingkuan Song +6
Text-to-3D (T23D) generation has emerged as a crucial visual generation task, aiming at synthesizing 3D content from textual descriptions. Studies of this task are currently shifti…
SeMv-3D: Towards Concurrency of Semantic and Multi-view Consistency in General Text-to-3D Generation
Xiao Cai, Pengpeng Zeng, Lianli Gao +3
General Text-to-3D (GT23D) generation is crucial for creating diverse 3D content across objects and scenes, yet it faces two key challenges: 1) ensuring semantic consistency betwee…
Training-Free Semantic Video Composition via Pre-trained Diffusion Model
Jiaqi Guo, Sitong Su, Junchen Zhu +2
The video composition task aims to integrate specified foregrounds and backgrounds from different videos into a harmonious composite. Current approaches, predominantly trained on v…
Make-A-Storyboard: A General Framework for Storyboard with Disentangled and Merged Control
Sitong Su, Litao Guo, Lianli Gao +2
Story Visualization aims to generate images aligned with story prompts, reflecting the coherence of storybooks through visual consistency among characters and scenes.Whereas curren…
F3-Pruning: A Training-Free and Generalized Pruning Strategy towards Faster and Finer Text-to-Video Synthesis
Sitong Su, Jianzhi Liu, Lianli Gao +1
Recently Text-to-Video (T2V) synthesis has undergone a breakthrough by training transformers or diffusion models on large-scale datasets. Nevertheless, inferring such large models…
MotionZero:Exploiting Motion Priors for Zero-shot Text-to-Video Generation
Sitong Su, Litao Guo, Lianli Gao +2
Zero-shot Text-to-Video synthesis generates videos based on prompts without any videos. Without motion information from videos, motion priors implied in prompts are vital guidance.…