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cs.CV2025
SARA: Structural and Adversarial Representation Alignment for Training-efficient Diffusion Models
Hesen Chen, Junyan Wang, Zhiyu Tan +1
Modern diffusion models encounter a fundamental trade-off between training efficiency and generation quality. While existing representation alignment methods, such as REPA, acceler…
cs.CV2025
Raccoon: Multi-stage Diffusion Training with Coarse-to-Fine Curating Videos
Zhiyu Tan, Junyan Wang, Hao Yang +4
Text-to-video generation has demonstrated promising progress with the advent of diffusion models, yet existing approaches are limited by dataset quality and computational resources…
cs.CV2025
LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment
Yibin Wang, Zhiyu Tan, Junyan Wang +3
Recent advances in text-to-video (T2V) generative models have shown impressive capabilities. However, these models are still inadequate in aligning synthesized videos with human pr…