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Animation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/soCzech/ShowHowTo

DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors

https://github.com/starfoe/Eye-bnb

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

https://github.com/sunzey/alphaclip

AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

https://github.com/talesofai/animatediff

AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

https://github.com/tommarvoloriddle/Bilinear-CNN-Tensorflow2.4-implementation

Bilinear CNNs for Fine-grained Visual Recognition

https://github.com/towhee-io/towhee

Omnivore: A Single Model for Many Visual Modalities

https://github.com/tu-rbo/concarne

Learning image representations tied to ego-motion

https://github.com/umangkeshri/JBM-classifying-defected-parts

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

https://github.com/wang-chen/SubT

Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

https://github.com/wang-chen/interestingness_ros

Visual Memorability for Robotic Interestingness via Unsupervised Online Learning

https://github.com/weiwang2330/siamese

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

https://github.com/xih108/Video_Completion

First Order Motion Model for Image Animation

https://github.com/xr0927/chapter5-learning_CSRNet

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

https://github.com/yhZhai/mcm

AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

https://github.com/zhuangshaobin/Video-GPT

Video-GPT via Next Clip Diffusion

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.