6 papers
OmniTryOn: Video Try-On Anything at Once!
Changliang Xia, Chengyou Jia, Minnan Luo +3
Although video virtual try-on (VVT) has achieved significant progress, existing methods still exhibit two fundamental limitations: first, they are restricted to single-garment tran…
PaCo-RL: Advancing Reinforcement Learning for Consistent Image Generation with Pairwise Reward Modeling
Bowen Ping, Chengyou Jia, Minnan Luo +4
Consistent image generation requires faithfully preserving identities, styles, and logical coherence across multiple images, which is essential for applications such as storytellin…
-Predictor: Noise-Free Deterministic Diffusion for Dense Prediction
Changliang Xia, Chengyou Jia, Minnan Luo +3
Although diffusion models with strong visual priors have emerged as powerful dense prediction backbones, they overlook a core limitation: the stochastic noise at the core of diffus…
From Ideal to Real: Unified and Data-Efficient Dense Prediction for Real-World Scenarios
Changliang Xia, Chengyou Jia, Zhuohang Dang +3
Dense prediction tasks hold significant importance of computer vision, aiming to learn pixel-wise annotated labels for input images. Despite advances in this field, existing method…
Why Settle for One? Text-to-ImageSet Generation and Evaluation
Chengyou Jia, Xin Shen, Zhuohang Dang +6
Despite remarkable progress in Text-to-Image models, many real-world applications require generating coherent image sets with diverse consistency requirements. Existing consistent…
ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
Chengyou Jia, Changliang Xia, Zhuohang Dang +3
Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the c…