Video Generation with Consistency Tuning
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Abstract (arXiv)
Currently, various studies have been exploring generation of long videos. However, the generated frames in these videos often exhibit jitter and noise. Therefore, in order to generate the videos without these noise, we propose a novel framework composed of four modules: separate tuning module, average fusion module, combined tuning module, and inter-frame consistency module. By applying our newly proposed modules subsequently, the consistency of the background and foreground in each video frames is optimized. Besides, the experimental results demonstrate that videos generated by our method exhibit a high quality in comparison of the state-of-the-art methods.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 16 references that are in the map. First posted on arXiv 11 Mar 2024.
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References · 14
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2023Segment Anythingcited by 8,234
- 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
- 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,778
- 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 961
- 2023Pix2Video: Video Editing using Image Diffusioncited by 320
- 2022MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and Interpolationcited by 309
- 2022Flexible Diffusion Modeling of Long Videoscited by 294
- 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
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