Video generation has recently emerged as a central task in the field of generative AI. However, the substantial computational cost inherent in video synthesis makes model distillation a critical technique for efficient deployment. Despite its significance, there is a scarcity of methods specifically designed for video diffusion models. Prevailing approaches often directly adapt image distillation techniques, which frequently lead to artifacts such as oversaturation, temporal inconsistency, and mode collapse. To address these challenges, we propose a novel distillation framework tailored specifically for video diffusion models. Its core innovations include: (1) an adaptive regression loss that dynamically adjusts spatial supervision weights to prevent artifacts arising from excessive distribution shifts; (2) a temporal regularization loss to counteract temporal collapse, promoting smooth and physically plausible sampling trajectories; and (3) an inference-time frame interpolation strategy that reduces sampling overhead while preserving perceptual quality. Extensive experiments and ablation studies on the VBench and VBench2 benchmarks demonstrate that our method achieves stable few-step video synthesis, significantly enhancing perceptual fidelity and motion realism. It consistently outperforms existing distillation baselines across multiple metrics.
Streaming Autoregressive Video Generation via Diagonal Distillation
South China University of Technology（南方科技大学）；Westlake University（西湖大学）；Johns Hopkins University（约翰霍普金斯大学）；University of California, Merced（加州大学默塞德分校）；The Chinese University of Hong Kong（香港中文大学）
Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation
Nanyang Technological University（南洋理工大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The State Key Lab of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
ConsDreamer: Advancing Multi-View Consistency for Zero-Shot Text-to-3D Generation
School of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息工程大学人工智能学院）；Department of Automation, Tsinghua University（清华大学自动化系）；Lenovo（联想）
Texas State University（德克萨斯州立大学）；Illinois Institute of Technology（伊利诺伊理工学院）
In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness. Compared to traditional forms of bias or discrimination caused by humans, algorithmic bias generated by AI is more abstract and unintuitive therefore more difficult to explain and mitigate. A clear gap exists in the current literature on evaluating and mitigating bias in pruned neural networks. In this work, we strive to tackle the challenging issues of evaluating, mitigating, and explaining induced bias in pruned neural networks. Our paper makes three contribu…
Flow-OPD: On-Policy Distillation for Flow Matching Models
University of Science and Technology of China（中国科学技术大学）；University of California, Los Angeles（加州大学洛杉矶分校）；The Chinese University of Hong Kong（香港中文大学）；Xiaohongshu Inc.（小红书公司）
Johns Hopkins University（约翰斯·霍普金斯大学）；Univ. of Maryland, College Park（马里兰大学帕克分校）；NIST（美国国家标准与技术研究院）
Face recognition networks generally demonstrate bias with respect to sensitive attributes like gender, skintone etc. For gender and skintone, we observe that the regions of the face that a network attends to vary by the category of an attribute. This might contribute to bias. Building on this intuition, we propose a novel distillation-based approach called Distill and De-bias (D&D) to enforce a network to attend to similar face regions, irrespective of the attribute category. In D&D, we train a teacher network on images from one category of an attribute; e.g. light skintone. Then distilling in…
DreamWaltz-G: Expressive 3D Gaussian Avatars from Skeleton-Guided 2D Diffusion
The University of Hong Kong (HKU)（香港大学）；Astribot（星动科技）；Tencent（腾讯）；University of Science and Technology of China (USTC)（中国科学技术大学）；International Digital Economy Academy (IDEA)（国际数字经济学院）
One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to tackle the problem of incremental learning. Most of these methods are based on knowledge distillation and do not adequately utilize the information provided by older task models, such as uncertainty estimation in predictions. The predictive uncertainty provides the distributional information can be applied to mitigate catastrophic forgetting in a deep learning framework. In the proposed work, we consider a Bayesian fo…
SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Sun Yat-sen University（中山大学）；Great Bay University（大贝大学）；Pazhou Lab (Huangpu)（琶洲实验室）；Guangdong Province Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）
Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（多媒体可信感知与高效计算重点实验室，中国教育部，厦门大学）；School of Medicine, Xiamen University（厦门大学医学院）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息电子学院）；Tencent Youtu Lab（腾讯优图实验室）；School of Computer Science and Technology, Ocean University of China（中国海洋大学计算机科学与技术学院）
SparkDiffusion: Mitigating the High-Sparsity Trap --- A Unified Framework for up to $265\times$ Single-GPU Acceleration of Visual Generation
Peking University（北京大学）；Melon Group；Tsinghua University（清华大学）；Alibaba Group（阿里巴巴集团）；University of Electronic Science and Technology of China（电子科技大学）；Harbin Institute of Technology（哈尔滨工业大学）
