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CoDeF: Content Deformation Fields for Temporally Consistent Video Processing abs: paper page: present the content deformation field CoDeF as a new type of video representation, which consists of a canonical content field aggregating the static contents in the entire video and a temporal deformation field recording the transformations from...

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AK

489,294 subscribers

153,241 Aufrufe • vor 3 Jahren •via X (Twitter)

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7 Kommentare

Profilbild von radAI
radAIvor 3 Jahren

This is a game-changer for video generation, faster and smother!!

Profilbild von Dan Rockwell
Dan Rockwellvor 3 Jahren

seriously dope, the speed of this and this ability will be massive in augmented reality

Profilbild von Don Jose Valle
Don Jose Vallevor 3 Jahren

Wow!

Profilbild von rogueyoshi (moving to fgc.network)
rogueyoshi (moving to fgc.network)vor 3 Jahren

@IXITimmyIXI

Profilbild von Takomo AI
Takomo AIvor 3 Jahren

Exciting new video representation technique!

Profilbild von Jose
Josevor 3 Jahren

As my boss says, tik-tok quality.

Profilbild von 张三丰
张三丰vor 3 Jahren

老师 为什么没有声音

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71,232 Aufrufe • vor 1 Jahr

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88,298 Aufrufe • vor 3 Monaten

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Google presents Still-Moving Customized Video Generation without Customized Video Data Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expanding this progress to video generation is still in its infancy, primarily due to the lack of customized video data. In this work, we introduce Still-Moving, a novel generic framework for customizing a text-to-video (T2V) model, without requiring any customized video data. The framework applies to the prominent T2V design where the video model is built over a text-to-image (T2I) model (e.g., via inflation). We assume access to a customized version of the T2I model, trained only on still image data (e.g., using DreamBooth or StyleDrop). Naively plugging in the weights of the customized T2I model into the T2V model often leads to significant artifacts or insufficient adherence to the customization data. To overcome this issue, we train lightweight Spatial Adapters that adjust the features produced by the injected T2I layers. Importantly, our adapters are trained on "frozen videos" (i.e., repeated images), constructed from image samples generated by the customized T2I model. This training is facilitated by a novel Motion Adapter module, which allows us to train on such static videos while preserving the motion prior of the video model. At test time, we remove the Motion Adapter modules and leave in only the trained Spatial Adapters. This restores the motion prior of the T2V model while adhering to the spatial prior of the customized T2I model. We demonstrate the effectiveness of our approach on diverse tasks including personalized, stylized, and conditional generation. In all evaluated scenarios, our method seamlessly integrates the spatial prior of the customized T2I model with a motion prior supplied by the T2V model.

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40,485 Aufrufe • vor 2 Jahren

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