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/zcymonkey/attt2m
공식 코드AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism
https://github.com/zhenzhiwang/intercontrol
공식 코드InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint
https://github.com/zixiangzhou916/ude
공식 코드https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel
Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels
https://github.com/Abdullah-Abuolaim/multi-task-defocus-deblurring-dual-pixel-nimat
Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels
https://github.com/Abdullah-Abuolaim/recurrent-defocus-deblurring-synth-dual-pixel
Multi-View Motion Synthesis via Applying Rotated Dual-Pixel Blur Kernels
https://github.com/DeepMotionEditing/deep-motion-editing
https://github.com/Djmcflush/Funky-Movement
https://github.com/EricGuo5513/text-to-motion
https://github.com/Mathux/TMR
TMR: Text-to-Motion Retrieval Using Contrastive 3D Human Motion Synthesis
https://github.com/Sohojoe/ActiveRagdollStyleTransfer
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/ThomasDupiereux/hpgan
https://github.com/Unity-Technologies/marathon-envs
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/YQRickWang/Motion_Prediction
https://github.com/YQRickWang/tf
https://github.com/bcv-uniandes/bodiffusion
BoDiffusion: Diffusing Sparse Observations for Full-Body Human Motion Synthesis
https://github.com/cai-jianfeng/ACTOR_mindspore
Action-Conditioned 3D Human Motion Synthesis with Transformer VAE
https://github.com/ebarsoum/hpgan
https://github.com/enriccorona/human-motion-prediction-pytorch
https://github.com/facebookresearch/QuaterNet
https://github.com/facebookresearch/multiface
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
https://github.com/fluide1022/MCM
MCM: Multi-condition Motion Synthesis Framework for Multi-scenario
https://github.com/garroud/human-motion-prediction-pytorch
https://github.com/gauraviiita/Visualization-of-Human3.6M-Dataset
https://github.com/jiashunwang/Long-term-Motion-in-3D-Scenes
Synthesizing Long-Term 3D Human Motion and Interaction in 3D Scenes
https://github.com/jpthu17/graphmotion
https://github.com/jw09191/MNET
https://github.com/kenfuliang/Awesome-Reinforcement-Learning
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/kwanyun/FFaceNeRF
AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models
https://github.com/line/Human-Interaction-Generation
https://github.com/nageshpindi/human-motion-prediction-master
https://github.com/nvlabs/dancing2music
https://github.com/p-morais/gym-cassie
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/pairlab/robosuite
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills
https://github.com/papagina/auto_conditioned_rnn_motion
Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.