2 citations · 3 across the 13 of their papers we have counts for
13 papers
Parallel Decoding Distillation for Fast Image and Video Generation
Neta Shaul, Chao Liu, Arash Vahdat +1
Generation in video diffusion or flow models is computationally expensive due to the slow and iterative sampling process. Current state-of-the-art (SOTA) acceleration methods heavi…
Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model
Xinyin Ma, Julius Berner, Chao Liu +3
Recent progress in large-scale generative models has substantially advanced video generation, yet existing methods remain constrained by a rigid inference paradigm. Bidirectional d…
Mode Seeking meets Mean Seeking for Fast Long Video Generation
Shengqu Cai, Weili Nie, Chao Liu +8
Scaling video generation from seconds to minutes faces a critical bottleneck: while short-video data is abundant and high-fidelity, coherent long-form data is scarce and limited to…
On the Adversarial Robustness of 3D Large Vision-Language Models
Chao Liu, Ngai-Man Cheung
3D Vision-Language Models (VLMs), such as PointLLM and GPT4Point, have shown strong reasoning and generalization abilities in 3D understanding tasks. However, their adversarial rob…
Transition Matching Distillation for Fast Video Generation
Weili Nie, Julius Berner, Nanye Ma +3
Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to…
Humanoid-inspired Causal Representation Learning for Domain Generalization
Ze Tao, Jian Zhang, Haowei Li +7
This paper proposes the Humanoid-inspired Structural Causal Model (HSCM), a novel causal framework inspired by human intelligence, designed to overcome the limitations of conventio…
On Equivariance and Fast Sampling in Video Diffusion Models Trained with Warped Noise
Chao Liu, Arash Vahdat
Temporally consistent video-to-video generation is critical for applications such as style transfer and upsampling. In this paper, we provide a theoretical analysis of warped noise…
Not-So-Optimal Transport Flows for 3D Point Cloud Generation
Ka-Hei Hui, Chao Liu, Xiaohui Zeng +2
Learning generative models of 3D point clouds is one of the fundamental problems in 3D generative learning. One of the key properties of point clouds is their permutation invarianc…
BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations
Weixi Feng, Chao Liu, Sifei Liu +3
Existing video generation models struggle to follow complex text prompts and synthesize multiple objects, raising the need for additional grounding input for improved controllabili…
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter
Chao Liu, Xin Liu, Zitong Yu +3
Deep neural networks (DNNs) have been applied in many computer vision tasks and achieved state-of-the-art (SOTA) performance. However, misclassification will occur when DNNs predic…
CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation
Dejia Xu, Weili Nie, Chao Liu +4
Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often…
Compositional Text-to-Image Generation with Dense Blob Representations
Weili Nie, Sifei Liu, Morteza Mardani +3
Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose…
SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models
Batu Ozturkler, Chao Liu, Benjamin Eckart +3
Diffusion models have recently gained popularity for accelerated MRI reconstruction due to their high sample quality. They can effectively serve as rich data priors while incorpora…