By inputting a single character image and template pose video, our method can generate vocal avatar videos featuring not only pose-accurate rendering but also realistic body shapes.
Given an input video and a simple user-provided text instruction describing the desired content, our method synthesizes dynamic objects or complex scene effects that naturally interact with the existing scene over time. The position, appearance, and motion of the new content are seamlessly integrated into the original footage while accounting for camera motion, occlusions, and interactions with other dynamic objects in the scene, resulting in a cohesive and realistic output video.
They propose an end-to-end multimodality-conditioned human video generation framework named OmniHuman, which can generate human videos based on a single human image and motion signals (e.g., audio only, video only, or a combination of audio and video). In OmniHuman, we introduce a multimodality motion conditioning mixed training strategy, allowing the model to benefit from data scaling up of mixed conditioning. This overcomes the issue that previous end-to-end approaches faced due to the scarcity of high-quality data. OmniHuman significantly outperforms existing methods, generating extremely realistic human videos based on weak signal inputs, especially audio. It supports image inputs of any aspect ratio, whether they are portraits, half-body, or full-body images, delivering more lifelike and high-quality results across various scenarios.
In the Golden Age of Hollywood (1930-1959), a 10:1 shooting ratio was the norm—a 90-minute film meant about 15 hours of footage. Directors like Alfred Hitchcock famously kept it tight with a 3:1 ratio, giving studios little wiggle room in the edit.
Fast forward to today: the digital era has sent shooting ratios skyrocketing. Affordable cameras roll endlessly, capturing multiple takes, resets, and everything in between. Gone are the disciplined “Action to Cut” days of film.
GOT-OCR2 works on a wide range of tasks, including plain document OCR, scene text OCR, formatted document OCR, and even OCR for tables, charts, mathematical formulas, geometric shapes, molecular formulas and sheet music.
Arminas created this using Juggernaut Xl model and QR Code Monster SDXL ControlNet.
His pipeline: Static Images – Forge UI. Upscaled with Leonardo AI universal upscaler. Animated with Runway ML and Minimax. Video upscale – Topaz Video AI. Composited in Adobe Premiere.
