chikap421/catlvdm
[ICLR 2026 - ReALM-GEN] This repository accompanies the paper "Corruption-Aware Training of Latent Video Diffusion Models for Robust Text-to-Video Generation"
This project helps video creators, marketers, and researchers generate high-quality video content from text descriptions, even when the input text or underlying data might have imperfections or 'noise'. It takes a text prompt as input and produces a short video clip. This is especially useful for professionals who need reliable video generation from diverse or less-than-perfect source materials.
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Use this if you need to reliably generate creative video content from text prompts, and you anticipate that your text inputs might be ambiguous, incomplete, or contain 'noise' from real-world data collection.
Not ideal if your primary concern is generating hyper-realistic video from perfectly clean, unambiguous prompts without any robustness considerations.
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Language
Python
License
MIT
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Last pushed
Jul 11, 2025
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