テキストから動画へのAIモデルの世界を革新する
人工知能の進歩により、特にテキストから動画モデルの領域において、創造性と革新の新しい時代が到来しました。これらの最先端モデルは、テキストのプロンプトに基づいてビデオを生成する驚異的な能力を持ち、アーティスト、映画製作者、コンテンツクリエイターに無限の可能性を開いています。結果が完璧でないかもしれませんが、過去2年間にわたるこれらのモデルの進化は非常に素晴らしいものでした。
One such model that has garnered considerable attention is Sora, created by OpenAI, the masterminds behind ChatGPT. Sora boasts a deep understanding of language and has the remarkable capability to generate compelling characters that express vibrant emotions. The videos produced by Sora have been hailed as hyper-realistic, leaving viewers in awe of its capabilities. Despite some minor hiccups, such as difficulties in maintaining smooth transitions and discerning left from right, Sora holds immense promise.
Google has also made significant strides in the field with their video generation AI, known as Lumiere. Powered by the innovative Space-Time-U-Net diffusion model, Lumiere excels at analyzing the spatial and temporal aspects of videos seamlessly. Unlike traditional models that stitch together individual frames like puzzle pieces, Lumiere tracks the movement and changes within a video, resulting in a smooth and coherent outcome. Although not yet available to the general public, Lumiere showcases Google’s prowess in AI video technology.
VideoPoet takes a unique approach to video generation, drawing inspiration from autoregressive language models. By training the model on a vast dataset of videos, images, audio, and text, VideoPoet can perform various video generation tasks with impressive proficiency. The model utilizes multiple tokenizers to bridge the gap between autoregressive language models and video generation, allowing it to understand and transform video, image, and audio clips into cohesive videos.
Meta’s Emu Video has gained recognition for its exceptional performance and surpassing commercial options. Through the optimization of noise schedules for diffusion and multi-stage training, Emu Video creates stunning videos from text and images. In evaluations, it outshone popular alternatives such as Google’s Imagen Video and NVIDIA’s PYOCO, captivating human evaluators with its unmatched quality.
Phenaki Video implements Mask GIT and PyTorch to generate text-guided videos. Its unique approach involves using an additional critic to guide the video-making process, providing a second opinion on what to focus on during sampling. This versatility makes Phenaki highly adaptable for research and training on both text-to-image and text-to-video tasks.
CogVideo, developed by researchers from the University of Tsinghua, leverages the knowledge acquired from a pre-trained text-to-image model to create an impressive text-to-video generative model. Notably, the model gained attention for its role in the creation of the critically acclaimed short film, “The Crow,” which even received recognition at the prestigious BAFTA Awards.
As text-to-video AI models continue to evolve, there is no doubt that they will revolutionize the creative landscape. These models offer unprecedented potential for artists and creators to bring their imaginations to life, paving the way for a new era of storytelling and visual expression. The future holds endless possibilities as these models continue to refine their capabilities and push the boundaries of what is possible in the realm of AI-generated videos.
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