StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text
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Abstract (arXiv)
Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video generation (typically 16 or 24 frames), ending up with hard-cuts when naively extended to the case of long video synthesis. To overcome these limitations, we introduce StreamingT2V, an autoregressive approach for long video generation of 80, 240, 600, 1200 or more frames with smooth transitions. The key components are:(i) a short-term memory block called conditional attention module (CAM), which conditions the current generation on the features extracted from the previous chunk via an attentional mechanism, leading to consistent chunk transitions, (ii) a long-term memory block called appearance preservation module, which extracts high-level scene and object features from the first video chunk to prevent the model from forgetting the initial scene, and (iii) a randomized blending approach that enables to apply a video enhancer autoregressively for infinitely long videos without inconsistencies between chunks. Experiments show that StreamingT2V generates high motion amount. In contrast, all competing image-to-video methods are prone to video stagnation when applied naively in an autoregressive manner. Thus, we propose with StreamingT2V a high-quality seamless text-to-long video generator that outperforms competitors with consistency and motion. Our code will be available at: https://github.com/Picsart-AI-Research/StreamingT2V
Key facts. Cited by 242 papers in the map (data through 7 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 45 of its 76 references that are in the map. First posted on arXiv 21 Mar 2024.
Builds on
45 of 76 references.
Closest ancestors · 5
Same direction, newest first
- 2024ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation6 Feb
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
- 2023Structure and Content-Guided Video Synthesis with Diffusion Models6 Feb
- 2021Learning Transferable Visual Models From Natural Language Supervision26 Feb
- 2020Denoising Diffusion Probabilistic Models19 Jun
Other references · 40
- 2017Attention Is All You Needcited by 59,932
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- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Classifier-Free Diffusion Guidancecited by 5,717
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
- 2017Neural Discrete Representation Learningcited by 4,650
- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
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- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
- 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,418
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- 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 666
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- 2023InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generationcited by 488
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- 2023VideoComposer: Compositional Video Synthesis with Motion Controllabilitycited by 461
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- 2023Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generationcited by 300
- 2023Emu Video: Factorizing Text-to-Video Generation by Explicit Image Conditioningcited by 254
- 2018Learning Blind Video Temporal Consistencycited by 247
- 2023SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Predictioncited by 231
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- 2023Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoisingcited by 135
- 2023VideoGen: A Reference-Guided Latent Diffusion Approach for High Definition Text-to-Video Generationcited by 74
- 2023ARTboldsymbol·V: Auto-Regressive Text-to-Video Generation with Diffusion Modelscited by 74
- 2023AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidancecited by 66
- 2023LivePhoto: Real Image Animation with Text-guided Motion Controlcited by 44
- 2023MEVG: Multi-event Video Generation with Text-to-Video Modelscited by 40
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Citing papers per year: 2024: 55; 2025: 89; 2026: 98.
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