most citedHiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

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cs.CV20251 cited

DCL-SE: Dynamic Curriculum Learning for Spatiotemporal Encoding of Brain Imaging

Meihua Zhou, Xinyu Tong, Jiarui Zhao +4

High-dimensional neuroimaging analyses for clinical diagnosis are often constrained by compromises in spatiotemporal fidelity and by the limited adaptability of large-scale, genera…

cs.CV2025

CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting

Lei Tian, Xiaomin Li, Liqian Ma +6

Recent advances in 3D reconstruction techniques and vision-language models have fueled significant progress in 3D semantic understanding, a capability critical to robotics, autonom…

cs.CV2024

StarVid: Enhancing Semantic Alignment in Video Diffusion Models via Spatial and SynTactic Guided Attention Refocusing

Yuanhang Li, Qi Mao, Lan Chen +5

Recent advances in text-to-video (T2V) generation with diffusion models have garnered significant attention. However, they typically perform well in scenes with a single object and…

cs.CV2024

UniVG: Towards UNIfied-modal Video Generation

Ludan Ruan, Lei Tian, Chuanwei Huang +2

Diffusion based video generation has received extensive attention and achieved considerable success within both the academic and industrial communities. However, current efforts ar…

cs.CV20242 cited

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

Hanzhang Wang, Haoran Wang, Jinze Yang +7

The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance…