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/liangxuy/Inter-X
공식 코드https://github.com/library87/OpenRoboExp
공식 코드https://github.com/lisiyao21/bailando
공식 코드Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory
https://github.com/llm-lab-org/generative-ai-for-character-animation-survey
공식 코드https://github.com/lpengyang/motionclone
공식 코드MotionClone: Training-Free Motion Cloning for Controllable Video Generation
https://github.com/markomih/VolumetricSMPL
공식 코드VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions
https://github.com/meaten/motionaug
공식 코드MotionAug: Augmentation with Physical Correction for Human Motion Prediction
https://github.com/michaelliyunhao/skeleton2humanoid
공식 코드Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening
https://github.com/mingyuan-zhang/FineMoGen
공식 코드FineMoGen: Fine-Grained Spatio-Temporal Motion Generation and Editing
https://github.com/mingyuan-zhang/MotionDiffuse
공식 코드MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
https://github.com/mingyuan-zhang/ReMoDiffuse
공식 코드https://github.com/minieval/citl
공식 코드https://github.com/monkeyseedocg/momo-code
공식 코드Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer
https://github.com/mrzzy2021/styledmotionsynthesis
공식 코드Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models
https://github.com/nate-gillman/self-correcting-self-consuming
공식 코드Self-Correcting Self-Consuming Loops for Generative Model Training
https://github.com/pabloruizponce/in2IN
공식 코드in2IN: Leveraging individual Information to Generate Human INteractions
https://github.com/patrickrperrine/comp-choreo
공식 코드KP-RNN: A Deep Learning Pipeline for Human Motion Prediction and Synthesis of Performance Art
https://github.com/pauzii/phasebetweener
공식 코드https://github.com/pr-ryan/demo
공식 코드Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning
https://github.com/priormdm/priormdm
공식 코드https://github.com/qrzou/parco
공식 코드https://github.com/rohollahhs/bad
공식 코드BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generation
https://github.com/shivangi-aneja/FaceTalk
공식 코드FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models
https://github.com/sigal-raab/modi
공식 코드https://github.com/steve-zeyu-zhang/MotionAnything
공식 코드https://github.com/steve-zeyu-zhang/MotionMamba
공식 코드https://github.com/synthesiaresearch/humanrf
공식 코드HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion
https://github.com/tr3e/intergen
공식 코드InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions
https://github.com/ubisoftinc/Ubisoft-LaForge-Animation-Dataset
공식 코드https://github.com/una-dinosauria/human-motion-prediction
공식 코드https://github.com/vita-epfl/motion-style-transfer
공식 코드Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion Forecasting
https://github.com/windvchen/sitcom-crafter
공식 코드Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes
https://github.com/wyysf-98/GenMM
공식 코드Example-based Motion Synthesis via Generative Motion Matching
https://github.com/xuehao-gao/guess
공식 코드GUESS:GradUally Enriching SyntheSis for Text-Driven Human Motion Generation
https://github.com/yhzhai/atom
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.