https://github.com/jsun57/comusion
공식 코드CoMusion: Towards Consistent Stochastic Human Motion Prediction via Motion Diffusion
Papers with Code 아카이브
Activity Recognition In Videos
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/junwenchen/GAP_SRAM
공식 코드Group Activity Prediction with Sequential Relational Anticipation Model
https://github.com/kaanakan/slamp
공식 코드https://github.com/kakaobrain/flame
공식 코드https://github.com/kay1794/mmvp-motion-matrix-based-video-prediction
공식 코드https://github.com/kit-mrt/future-motion
공식 코드https://github.com/kit-mrt/red-motion
공식 코드https://github.com/kit-mrt/road-barlow-twins
공식 코드https://github.com/kowoonho/arfree
공식 코드Autoregression-free video prediction using diffusion model for mitigating error propagation
https://github.com/koyumizutani/earthquake
공식 코드https://github.com/kth-rpl/detect_vlm
공식 코드Hard Cases Detection in Motion Prediction by Vision-Language Foundation Models
https://github.com/kth-rpl/rmp
공식 코드https://github.com/kwwcv/selfmotion
공식 코드https://github.com/kwwcv/ssmp
공식 코드Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
https://github.com/l1bra1/rigidflow
공식 코드Self-Supervised 3D Scene Flow Estimation and Motion Prediction using Local Rigidity Prior
https://github.com/leob03/mdmp
공식 코드MDMP: Multi-modal Diffusion for supervised Motion Predictions with uncertainty
https://github.com/lily2lab/deepssm
공식 코드DeepSSM: Deep State-Space Model for 3D Human Motion Prediction
https://github.com/linghaochan/humanmac
공식 코드HumanMAC: Masked Motion Completion for Human Motion Prediction
https://github.com/longzhong-lin/eda
공식 코드EDA: Evolving and Distinct Anchors for Multimodal Motion Prediction
https://github.com/m-hasan-n/roundabout
공식 코드https://github.com/magnux/MotionGAN
공식 코드https://github.com/maoyunyao/mamp
공식 코드Masked Motion Predictors are Strong 3D Action Representation Learners
https://github.com/map-learning/tl2la
공식 코드Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data
https://github.com/meaten/motionaug
공식 코드MotionAug: Augmentation with Physical Correction for Human Motion Prediction
https://github.com/mediabrain-sjtu/auxformer
공식 코드Auxiliary Tasks Benefit 3D Skeleton-based Human Motion Prediction
https://github.com/mediabrain-sjtu/eqmotion
공식 코드EqMotion: Equivariant Multi-agent Motion Prediction with Invariant Interaction Reasoning
https://github.com/mediabrain-sjtu/jrtransformer
공식 코드Joint-Relation Transformer for Multi-Person Motion Prediction
https://github.com/mediabrain-sjtu/spgsn
공식 코드Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction
https://github.com/mhnasseri/sort_oh
공식 코드Simple online and real-time tracking with occlusion handling
https://github.com/michigan-traffic-lab/DECODE
공식 코드DECODE: Domain-aware Continual Domain Expansion for Motion Prediction
https://github.com/mimbres/SeqSkip
공식 코드Sequential Skip Prediction with Few-shot in Streamed Music Contents
https://github.com/mkaep/transformer-explainability
공식 코드Attention Please: What Transformer Models Really Learn for Process Prediction
https://github.com/ml-jku/clamp
공식 코드Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language
https://github.com/mmahdavian/stpotr
공식 코드https://github.com/mohsenzand/motionflow
공식 코드Flow-based Spatio-Temporal Structured Prediction of Motion Dynamics
https://github.com/nachiket92/PGP
공식 코드Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.