![]() On November 2nd, another Redditor posted a photo claiming he switched out his boss’ family photos with Astronaut Sloth (shown below, right). The following day, a screenshot of her post was shared on Reddit, gaining 27,581 upvotes and 2,045 votes overall. Thirty minutes later, she noted that his phone was password protected so she placed a framed photo of the sloth on his desk (shown below, left). ![]() On October 29th, Tumblr user alpacalypse made a post stating that her father got mad at her for changing all of his account photos and device backgrounds to the sloth photo. The original photo as well as its derivative instances (shown below) can be found on Tumblr under the tags space sloth, astronaut sloth and astrosloth. Two Facebook fan pages were created in May and June that same year with just over 100 likes between them as of November 2012. Ten days later, a novelty Twitter account for the sloth was created. On February 17th, the photo was featured as part of a caption contest on Cheezburger’s Animal Capshunz site. ” The post earned 3,345 upvotes and 1,170 points overall. Two days later, it was shared on the /r/Trees subreddit with the title “Smoking hash out of my friends bong, how I feel at a. It was reposted to Tumblr by queenofsloths on February 8th, 2012, where it was far better received with 9,237 notes over the course of ten months. However, there is a risk of misuse for creating fake or harmful content with our technology, and we believe that it is crucial to develop and apply tools for detecting biases and malicious use cases in order to ensure a safe and fair use.The day after the photo was shared on deviantArt, Dionísio reposted it to his Tumblr, where it earned a modest number of 112 notes. Our primary goal in this work is to enable novice users to generate visual content in an creative and flexible way. The Great Wave of Kanagava, public domain. Girl with a Pearl Earring, public domain. Raising the Flag on Iwo Jima, public domain. We thank owners of images and videos used in our experiments ( links for attribution) for sharing their valuable assets. Freeman and David Salesin for their collaboration, helpful discussions, feedback and support. We would like to thank Ronny Votel, Orly Liba, Hamid Mohammadi, April Lehman, Bryan Seybold, David Ross, Dan Goldman, Hartwig Adam, Xuhui Jia, Xiuye Gu, Mehek Sharma, Keyu Zhang, Rachel Hornung, Oran Lang, Jess Gallegos, William T. (*): Equal first co-author, (†) Core technical contribution ![]() ![]() We demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation. By deploying both spatial and (importantly) temporal down- and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution - an approach that inherently makes global temporal consistency difficult to achieve. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. We introduce Lumiere - a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion - a pivotal challenge in video synthesis. ![]()
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