Papers with Code 아카이브
Video Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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<span style="color:grey; opacity: 0.6">( Various Video Generation Tasks. Gif credit: MaGViT )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis
CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation
Contextual Gesture: Co-Speech Gesture Video Generation through Context-aware Gesture Representation
Articulate That Object Part (ATOP): 3D Part Articulation from Text and Motion Personalization
Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated Videos
Enhance-A-Video: Better Generated Video for Free
VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation
Magic 1-For-1: Generating One Minute Video Clips within One Minute
Next Block Prediction: Video Generation via Semi-Auto-Regressive Modeling
Efficient-vDiT: Efficient Video Diffusion Transformers With Attention Tile
CustomVideoX: 3D Reference Attention Driven Dynamic Adaptation for Zero-Shot Customized Video Diffusion Transformers
TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
Señorita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists
History-Guided Video Diffusion
Conditional diffusion model with spatial attention and latent embedding for medical image segmentation
A Physical Coherence Benchmark for Evaluating Video Generation Models via Optical Flow-guided Frame Prediction
HumanDiT: Pose-Guided Diffusion Transformer for Long-form Human Motion Video Generation
Goku: Flow Based Video Generative Foundation Models
FlashVideo:Flowing Fidelity to Detail for Efficient High-Resolution Video Generation
UniForm: A Unified Multi-Task Diffusion Transformer for Audio-Video Generation
Content-Rich AIGC Video Quality Assessment via Intricate Text Alignment and Motion-Aware Consistency
Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression
MotionCanvas: Cinematic Shot Design with Controllable Image-to-Video Generation
UniCP: A Unified Caching and Pruning Framework for Efficient Video Generation
Fast Video Generation with Sliding Tile Attention
MotionAgent: Fine-grained Controllable Video Generation via Motion Field Agent
FreqPrior: Improving Video Diffusion Models with Frequency Filtering Gaussian Noise
Towards Physical Understanding in Video Generation: A 3D Point Regularization Approach
On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile Devices
IPO: Iterative Preference Optimization for Text-to-Video Generation
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
Harness Local Rewards for Global Benefits: Effective Text-to-Video Generation Alignment with Patch-level Reward Models
OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models
VidSketch: Hand-drawn Sketch-Driven Video Generation with Diffusion Control
Improved Training Technique for Latent Consistency Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.