https://github.com/hellovivian/generative-disco
공식 코드Generative Disco: Text-to-Video Generation for Music Visualization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Ma grand-mère m’a raconté que quand elle était étudiante, elle avait un petit-ami. À l’âge de 18 ans, il a dû partir pour le service militaire, elle ne l’a pas attendu et elle a épousé quelqu’un d’autre. Quand ma grand-mère avait 58-59 ans, un homme (son premier amour) lui a envoyé une demande d’amis sur un réseau social, ils ont commencé à parler... En moins de six mois, ils ont décidé de se voir. Le trajet en train a duré deux jours et ils se sont finalement rencontrés. Cela fait maintenant deux ans qu’ils habitent ensemble et qu’ils nous rendent visite de temps en temps. Je réalise maintenant que leur amour l’un envers l’autre n’a jamais cessé.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Generative Disco: Text-to-Video Generation for Music Visualization
TALC: Time-Aligned Captions for Multi-Scene Text-to-Video Generation
LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation
GGAvatar: Reconstructing Garment-Separated 3D Gaussian Splatting Avatars from Monocular Video
AvatarGen: A 3D Generative Model for Animatable Human Avatars
FIFO-Diffusion: Generating Infinite Videos from Text without Training
Pyramidal Flow Matching for Efficient Video Generative Modeling
DirecT2V: Large Language Models are Frame-Level Directors for Zero-Shot Text-to-Video Generation
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
MMTrail: A Multimodal Trailer Video Dataset with Language and Music Descriptions
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
MotionClone: Training-Free Motion Cloning for Controllable Video Generation
InfLVG: Reinforce Inference-Time Consistent Long Video Generation with GRPO
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos
Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation
VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting
Evaluation of Text-to-Video Generation Models: A Dynamics Perspective
VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation
MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
ConditionVideo: Training-Free Condition-Guided Text-to-Video Generation
StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation
SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset
MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation
Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning
Sketching the Future (STF): Applying Conditional Control Techniques to Text-to-Video Models
VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation
Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.