Oh, look who’s trying to be the next Steven Spielberg without even lifting a finger. Text-driven video editing is all the rage these days, with its goal of creating new videos from text prompts and existing footage without any manual labor. Sounds like a dream come true for the social media mavens, marketers, and advertisers who are always on the lookout for the next big thing. But let’s pump the brakes for a second, shall we?
For this cinematic magic trick to work, the output must be an Oscar-worthy masterpiece that accurately reflects the original video, keeps the created frames dancing in perfect harmony, and aligns with the target prompts. Easy as pie, right? Well, not quite. The catch? It takes a boatload of computing power to train a text-to-video model using just heaps of text-video data. So, what’s a budding movie mogul to do?
Enter zero-shot and one-shot text-driven video editing approaches, which have been riding the wave of recent advancements in large-scale text-to-image diffusion models and programmable picture editing. And guess what? They don’t even need extra video data. These clever little innovations have shown they can tweak films in response to a variety of text commands. But, alas, there’s always a catch.
Despite the ginormous strides in getting the output to play nice with text cues, empirical data shows that current methods are still struggling to keep up appearances and maintain temporal consistency. It’s like trying to juggle flaming swords while riding a unicycle impressive, but not without its risks.
Never fear, though, because researchers from Tsinghua University, Renmin University of China, ShengShu, and Pazhou Laboratory are here to save the day with ControlVideo, a shiny new method based on a pretrained text-to-image diffusion model for faithful and reliable text-driven video editing. Movie magic, here we come!
Taking a page out of ControlNet’s book, ControlVideo steps up its game by incorporating visual conditions like Canny edge maps, HED borders, and depth maps for all frames as extra inputs. A ControlNet pretrained on the diffusion model handles these visual curveballs, putting them in the ring with the text and attention-based tactics currently in use.
So, who needs a film crew when you’ve got a cutting-edge method like ControlVideo in your corner? Just sit back, relax, and watch the text-driven video editing revolution unfold before your very eyes. And maybe, just maybe, you’ll be the one accepting that Best Director trophy someday no manual labor required. […]
