Video Generation from Text Employing Latent Path Construction for Temporal Modeling
Explore this paper's citation graph
- Type
- article
- Published
- 2021-07-29
- Cited by
- 7
- References
- 44
- Access
- Open access
- OpenAlex
- https://openalex.org/W3186818465
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:236493418
Keywords
Computer science, Artificial intelligence, Natural language generation, Context (archaeology), Natural language
References
- UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
- Whistler: a trainable text-to-speech system
- Can humans fly? Action understanding with multiple classes of actors
- ImageNet: A large-scale hierarchical image database
- Improved Techniques for Training GANs
- Learning to Generate Long-term Future via Hierarchical Prediction
- Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
- The Kinetics Human Action Video Dataset
- MoCoGAN: Decomposing Motion and Content for Video Generation
- Attentive Semantic Video Generation Using Captions
- Video Generation From Text
- Actor and Action Video Segmentation from a Sentence
- VOS-GAN: Adversarial Learning of Visual-Temporal Dynamics for Unsupervised Dense Prediction in Videos
- Stochastic Adversarial Video Prediction
- Everybody Dance Now
- Pay Attention! - Robustifying a Deep Visuomotor Policy Through Task-Focused Visual Attention
- Multi-modal Capsule Routing for Actor and Action Video Segmentation Conditioned on Natural Language Queries
- Animating Arbitrary Objects via Deep Motion Transfer
- Deep Video Inpainting
- Edge-Aware Deep Image Deblurring
Cited by
- MUGEN: A Playground for Video-Audio-Text Multimodal Understanding and GENeration
- Role of Artificial Intelligence in Generating Video
- A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint
- TEXT2AV – Automated Text to Audio and Video Conversion
- Text to video generation via knowledge distillation
- Using Artificial Intelligence in Digital Video Production: A Systematic Review Study
- Multidimensional Text-to-Video Generation: Integrating Sentiment, Pragmatics, and Semantics
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