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Found 18 papers, 12 papers with code
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
To bridge this gap, we introduce VBench-2. 0, a next-generation benchmark designed to automatically evaluate video generative models for their intrinsic faithfulness.
RepVideo: Rethinking Cross-Layer Representation for Video Generation
Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos.
Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models
We present Vchitect-2. 0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation.
Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models
Moreover, existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations.
VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge.
WiCV@CVPR2024: The Thirteenth Women In Computer Vision Workshop at the Annual CVPR Conference
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2024, organized alongside the CVPR 2024 in Seattle, Washington, United States.
FreeInit: Bridging Initialization Gap in Video Diffusion Models
Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics.
VBench: Comprehensive Benchmark Suite for Video Generative Models
We will open-source VBench, including all prompts, evaluation methods, generated videos, and human preference annotations, and also include more video generation models in VBench to drive forward the field of video generation.
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model.
Ranked #4 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
WiCV@CVPR2023: The Eleventh Women In Computer Vision Workshop at the Annual CVPR Conference
In this paper, we present the details of Women in Computer Vision Workshop - WiCV 2023, organized alongside the hybrid CVPR 2023 in Vancouver, Canada.
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML).
Collaborative Diffusion for Multi-Modal Face Generation and Editing
In this work, we present Collaborative Diffusion, where pre-trained uni-modal diffusion models collaborate to achieve multi-modal face generation and editing without re-training.
ReVersion: Diffusion-Based Relation Inversion from Images
In this work, we propose the Relation Inversion task, which aims to learn a specific relation (represented as "relation prompt") from exemplar images.
DS3-Net: Difficulty-perceived Common-to-T1ce Semi-Supervised Multimodal MRI Synthesis Network
Furthermore, with only 5% paired data, the proposed DS3-Net achieves competitive performance with state-of-theart image translation methods utilizing 100% paired data, delivering an average SSIM of 0. 8947 and an average PSNR of 23. 60.
Multi-modal Brain Tumor Segmentation via Missing Modality Synthesis and Modality-level Attention Fusion
As such, it is clinically meaningful to develop a method to synthesize unavailable modalities which can also be used as additional inputs to downstream tasks (e. g., brain tumor segmentation) for performance enhancing.
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
The two models use labeled data (together with the corresponding transferred images) for supervised learning and perform collaborative consistency learning on unlabeled data.
Talk-to-Edit: Fine-Grained Facial Editing via Dialog
In this work, we propose Talk-to-Edit, an interactive facial editing framework that performs fine-grained attribute manipulation through dialog between the user and the system.
Ranked #1 on
Fine-Grained Facial Editing
on CelebA-Dialog
