Papers with Code 아카이브
Motion Synthesis
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 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/bandainamcoresearchinc/bandai-namco-research-motiondataset
공식 코드Motion Capture Dataset for Practical Use of AI-based Motion Editing and Stylization
https://github.com/boeun-kim/most
공식 코드MoST: Motion Style Transformer between Diverse Action Contents
https://github.com/boreshkinai/delta-interpolator
공식 코드https://github.com/brachiation-rl/brachiation
공식 코드https://github.com/briqr/recurrent_transformer_vae_multi_action
공식 코드Recurrent Transformer Variational Autoencoders for Multi-Action Motion Synthesis
https://github.com/bujiazi/motionclone
공식 코드MotionClone: Training-Free Motion Cloning for Controllable Video Generation
https://github.com/chaiyujin/glow-pytorch
공식 코드MoGlow: Probabilistic and controllable motion synthesis using normalising flows
https://github.com/chenfengye/motion-latent-diffusion
공식 코드Executing your Commands via Motion Diffusion in Latent Space
https://github.com/christsa/dgrasp
공식 코드D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions
https://github.com/cr7anand/neural_temporal_models
공식 코드https://github.com/cure-lab/MotionCraft
공식 코드MotionCraft: Crafting Whole-Body Motion with Plug-and-Play Multimodal Controls
https://github.com/degardinbruno/kinetic-gan
공식 코드Generative Adversarial Graph Convolutional Networks for Human Action Synthesis
https://github.com/dehezhang2/point_based_nerf_editing
공식 코드Point-Based Radiance Fields for Controllable Human Motion Synthesis
https://github.com/dmoltisanti/brace
공식 코드BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis
https://github.com/electronicarts/character-motion-vaes
공식 코드https://github.com/exitudio/BAMM
공식 코드https://github.com/exitudio/MMM
공식 코드https://github.com/facebookresearch/meshtalk
공식 코드MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
https://github.com/frank-zy-dou/emdm
공식 코드EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation
https://github.com/gloria2tt/t3m
공식 코드https://github.com/godzillalla/dance-synthesis-project
공식 코드Diverse Dance Synthesis via Keyframes with Transformer Controllers
https://github.com/gohar-malik/intermask
공식 코드InterMask: 3D Human Interaction Generation via Collaborative Masked Modelling
https://github.com/google-research/mint
공식 코드AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
https://github.com/guytevet/motion-diffusion-model
공식 코드https://github.com/hongfz16/avatarclip
공식 코드AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars
https://github.com/iPaw-AI-LAB/LawDNet_2024
공식 코드LawDNet: Enhanced Audio-Driven Lip Synthesis via Local Affine Warping Deformation
https://github.com/inventec-ai-center/carl-siggraph2020
공식 코드CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion
https://github.com/jiashunwang/MRT
공식 코드https://github.com/jihoonerd/Conditional-Motion-In-Betweening
공식 코드https://github.com/jnnan/chairs
공식 코드https://github.com/jonepatr/lets_face_it
공식 코드https://github.com/kakaobrain/flame
공식 코드https://github.com/kumapowerliu/human-motionformer
공식 코드Human MotionFormer: Transferring Human Motions with Vision Transformers
https://github.com/kwanyun/AnyMoLe
공식 코드AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models
https://github.com/li-ronghui/FineDance
공식 코드FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.