2 citations · 7 across the 16 of their papers we have counts for
16 papers
Causal-rCM: A Unified Teacher-Forcing and Self-Forcing Open Recipe for Autoregressive Diffusion Distillation in Streaming Video Generation and Interactive World Models
Kaiwen Zheng, Guande He, Min Zhao +7
Autoregressive video diffusion with causal diffusion transformers has emerged as a major paradigm for real-time streaming video generation and action-conditioned interactive world…
minWM: A Full-Stack Open-Source Framework for Real-Time Interactive Video World Models
Min Zhao, Hongzhou Zhu, Bokai Yan +13
Recent video diffusion foundation models have achieved remarkable progress in high-quality video generation, yet turning them into real-time interactive video world models remains…
Causal Forcing: Autoregressive Diffusion Distillation Done Right for High-Quality Real-Time Interactive Video Generation
Hongzhou Zhu, Min Zhao, Guande He +3
To achieve real-time interactive video generation, current methods distill pretrained bidirectional video diffusion models into few-step autoregressive (AR) models, facing an archi…
UltraViCo: Breaking Extrapolation Limits in Video Diffusion Transformers
Min Zhao, Hongzhou Zhu, Yingze Wang +6
Despite advances, video diffusion transformers still struggle to generalize beyond their training length, a challenge we term video length extrapolation. We identify two failure mo…
Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN Discriminator
Kaiwen Zheng, Yongxin Chen, Huayu Chen +4
While likelihood-based generative models, particularly diffusion and autoregressive models, have achieved remarkable fidelity in visual generation, the maximum likelihood estimatio…
RIFLEx: A Free Lunch for Length Extrapolation in Video Diffusion Transformers
Min Zhao, Guande He, Yixiao Chen +3
Recent advancements in video generation have enabled models to synthesize high-quality, minute-long videos. However, generating even longer videos with temporal coherence remains a…
Elucidating the Preconditioning in Consistency Distillation
Kaiwen Zheng, Guande He, Jianfei Chen +2
Consistency distillation is a prevalent way for accelerating diffusion models adopted in consistency (trajectory) models, in which a student model is trained to traverse backward o…
Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models
Zijun Chen, Wenbo Hu, Guande He +3
Multimodal large language models (MLLMs) combine visual and textual data for tasks such as image captioning and visual question answering. Proper uncertainty calibration is crucial…
Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models
Peng Cui, Guande He, Dan Zhang +3
Datasets collected from the open world unavoidably suffer from various forms of randomness or noiseness, leading to the ubiquity of aleatoric (data) uncertainty. Quantifying such u…
Consistency Diffusion Bridge Models
Guande He, Kaiwen Zheng, Jianfei Chen +2
Diffusion models (DMs) have become the dominant paradigm of generative modeling in a variety of domains by learning stochastic processes from noise to data. Recently, diffusion den…
Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models
Fan Bao, Chendong Xiang, Gang Yue +7
We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-Vi…
Diffusion Bridge Implicit Models
Kaiwen Zheng, Guande He, Jianfei Chen +2
Denoising diffusion bridge models (DDBMs) are a powerful variant of diffusion models for interpolating between two arbitrary paired distributions given as endpoints. Despite their…
Noise Contrastive Alignment of Language Models with Explicit Rewards
Huayu Chen, Guande He, Lifan Yuan +3
User intentions are typically formalized as evaluation rewards to be maximized when fine-tuning language models (LMs). Existing alignment methods, such as Direct Preference Optimiz…
Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis
Zehua Chen, Guande He, Kaiwen Zheng +2
In text-to-speech (TTS) synthesis, diffusion models have achieved promising generation quality. However, because of the pre-defined data-to-noise diffusion process, their prior dis…
Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting
Guande He, Peng Cui, Jianfei Chen +2
Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-…
Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models
Guande He, Jianfei Chen, Jun Zhu
Large pre-trained language models (PLMs) have demonstrated strong performance on natural language understanding (NLU) tasks through fine-tuning. However, fine-tuned models still su…