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20232025
most citedStyleShot: A Snapshot on Any Style

2 citations · 4 across the 9 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CV2024

Live2Diff: Live Stream Translation via Uni-directional Attention in Video Diffusion Models

Zhening Xing, Gereon Fox, Yanhong Zeng +4

Large Language Models have shown remarkable efficacy in generating streaming data such as text and audio, thanks to their temporally uni-directional attention mechanism, which mode…

cs.CV2024

HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation

Zhenzhi Wang, Yixuan Li, Yanhong Zeng +8

Human image animation involves generating videos from a character photo, allowing user control and unlocking the potential for video and movie production. While recent approaches y…

cs.CV2024

FoleyCrafter: Bring Silent Videos to Life with Lifelike and Synchronized Sounds

Yiming Zhang, Yicheng Gu, Yanhong Zeng +4

We study Neural Foley, the automatic generation of high-quality sound effects synchronizing with videos, enabling an immersive audio-visual experience. Despite its wide range of ap…

cs.CV2024★ 2 cited

StyleShot: A Snapshot on Any Style

Junyao Gao, Yanchen Liu, Yanan Sun +4

In this paper, we show that, a good style representation is crucial and sufficient for generalized style transfer without test-time tuning. We achieve this through constructing a s…

eess.IV2024

Sagiri: Low Dynamic Range Image Enhancement with Generative Diffusion Prior

Baiang Li, Sizhuo Ma, Yanhong Zeng +5

Capturing High Dynamic Range (HDR) scenery using 8-bit cameras often suffers from over-/underexposure, loss of fine details due to low bit-depth compression, skewed color distribut…

cs.CV2024

Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language

Yicheng Chen, Xiangtai Li, Yining Li +4

Diffusion models can generate realistic and diverse images, potentially facilitating data availability for data-intensive perception tasks. However, leveraging these models to boos…

cs.CV2024

Make-It-Vivid: Dressing Your Animatable Biped Cartoon Characters from Text

Junshu Tang, Yanhong Zeng, Ke Fan +4

Creating and animating 3D biped cartoon characters is crucial and valuable in various applications. Compared with geometry, the diverse texture design plays an important role in ma…