Few-step text-to-image models increasingly replace slower generators, yet acceleration can silently change distributions over unspecified attributes even when individual outputs remain plausible and aligned. We call these distributions semantic defaults and their change under replacement semantic default shift. Existin...
Xuan-Hua Yin, Chuanzhi Xu, Shunqi Mao et al.· 2 citations
This work introduces ReAL, a training-free sampler that selects how far to advance using one shared lookahead, and shows that the discrepancy between uncorrected and lookahead-corrected endpoint proposals can be computed directly from the observed velocity mismatch and the candidate span beyond the lookahead.
Xuan-Hua Yin, Chuanzhi Xu, Hao-Xian Zhou et al.· 0 citations
PrefLUT is introduced, a reusable and refinable user-preference modeling framework for deployable 3D LUTs, encoding ordered preferred/non-preferred image pairs into a lightweight Reusable User Profile that is reused across queries and refined using additional user preference pairs, without per-user optimization.
Chuanzhi Xu, Lang-Yi Chen, Cheng-Kun Yue et al.· 0 citations
Aes3D, the first systematic framework for assessing the aesthetics of 3D neural rendering scenes, is proposed, and Aes3DGSNet, a lightweight model that directly predicts scene-level aesthetic scores from 3DGS representations, is presented, a lightweight model that operates solely on 3D Gaussian primitives.
