https://github.com/BarqueroGerman/BeLFusion
공식 코드BeLFusion: Latent Diffusion for Behavior-Driven Human Motion Prediction
Papers with Code 아카이브
Motion Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/BarqueroGerman/FlowMDM
공식 코드Seamless Human Motion Composition with Blended Positional Encodings
https://github.com/DK-Jang/motion_puzzle
공식 코드https://github.com/Dai-Wenxun/MotionLCM
공식 코드MotionLCM: Real-time Controllable Motion Generation via Latent Consistency Model
https://github.com/DeepGesture/DeepGesture-Unity
공식 코드DeepGesture: A conversational gesture synthesis system based on emotions and semantics
https://github.com/EricGuo5513/TM2T
공식 코드TM2T: Stochastic and Tokenized Modeling for the Reciprocal Generation of 3D Human Motions and Texts
https://github.com/EricGuo5513/momask-codes
공식 코드https://github.com/FuxiCV/SSMCT
공식 코드https://github.com/Garfield-kh/TM2D
공식 코드TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration
https://github.com/Godheritage/BOTH2Hands
공식 코드BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics
https://github.com/HaiminLuo/Artemis
공식 코드Artemis: Articulated Neural Pets with Appearance and Motion synthesis
https://github.com/Hxxxz0/Free-T2m
공식 코드Free-T2M: Frequency Enhanced Text-to-Motion Diffusion Model With Consistency Loss
https://github.com/IDEA-Research/HumanTOMATO
공식 코드https://github.com/Khrylx/RFC
공식 코드Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis
https://github.com/Mael-zys/T2M-GPT
공식 코드T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations
https://github.com/Mathux/ACTOR
공식 코드Action-Conditioned 3D Human Motion Synthesis with Transformer VAE
https://github.com/Mathux/TEMOS
공식 코드TEMOS: Generating diverse human motions from textual descriptions
https://github.com/NVlabs/Dance2Music
공식 코드https://github.com/PeizhuoLi/ganimator
공식 코드https://github.com/PolarisDane/KETA
공식 코드KETA: Kinematic-Phrases-Enhanced Text-to-Motion Generation via Fine-grained Alignment
https://github.com/RAISELab-atUVA/Projected-Diffusion
공식 코드https://github.com/Sirui-Xu/InterDiff
공식 코드InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion
https://github.com/Stanford-TML/EDGE
공식 코드https://github.com/Yi-Shi94/AMDM
공식 코드Interactive Character Control with Auto-Regressive Motion Diffusion Models
https://github.com/ZeyuLing/MotionLLaMA
공식 코드MotionLLaMA: A Unified Framework for Motion Synthesis and Comprehension
https://github.com/ZikangLeng/IMUGPT
공식 코드https://github.com/abhinanda-punnakkal/BABEL
공식 코드https://github.com/afford-motion/afford-motion
공식 코드Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordance
https://github.com/aioz-ai/AIOZ-GDANCE
공식 코드https://github.com/aioz-ai/GCD
공식 코드https://github.com/alessiosam/ladiff
공식 코드https://github.com/amathislab/kinesis
공식 코드https://github.com/anindita127/IMoS
공식 코드IMos: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactions
https://github.com/anindita127/ReMoS
공식 코드ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions
https://github.com/anindita127/complextext2animation
공식 코드Synthesis of Compositional Animations from Textual Descriptions
https://github.com/anindita127/duetgen
공식 코드DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.