https://github.com/simonalexanderson/MoGlow
MoGlow: Probabilistic and controllable motion synthesis using normalising flows
Papers with Code 아카이브
Motion Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 13
- 보존된 평가 기록
- 123
Creating a video where people in the images move (such as blinking or smiling) requires specialized AI technology like Deepfake or Motion Synthesis, which I cannot do directly here.
However, if you want a video made from the images you provided, I can create an animated slideshow with cute effects and royalty-free background music. Would you be interested in this?
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/simonalexanderson/StyleGestures
MoGlow: Probabilistic and controllable motion synthesis using normalising flows
https://github.com/viiika/diffusion-conductor
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
https://github.com/xbpeng/DeepMimic
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/youngseng/diffusestylegesture
Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.