17 papers
Class-frequency Guided Noise Schedule for Diffusion Models
Jiequan Cui, Beier Zhu, Qingshan Xu +3
In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, l…
From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information
Junbao Zhou, Qingshan Xu, Yuan Zhou +7
3D Gaussian Splatting (3DGS) has emerged as a promising technique for novel view synthesis. However, 3DGS requires dense input views to achieve high-quality rendering. In sparse-vi…
Generalized Kullback-Leibler Divergence Loss
Jiequan Cui, Beier Zhu, Qingshan Xu +5
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss…
Streaming Drag-Oriented Interactive Video Manipulation: Drag Anything, Anytime!
Junbao Zhou, Yuan Zhou, Kesen Zhao +4
Achieving streaming, fine-grained control over the outputs of autoregressive video diffusion models remains challenging, making it difficult to ensure that they consistently align…
MuSteerNet: Human Reaction Generation from Videos via Observation-Reaction Mutual Steering
Yuan Zhou, Yongzhi Li, Yanqi Dai +6
Video-driven human reaction generation aims to synthesize 3D human motions that directly react to observed video sequences, which is crucial for building human-like interactive AI…
Real-Time Motion-Controllable Autoregressive Video Diffusion
Kesen Zhao, Jiaxin Shi, Beier Zhu +5
Real-time motion-controllable video generation remains challenging due to the inherent latency of bidirectional diffusion models and the lack of effective autoregressive (AR) appro…