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We introduce W.A.L.T, a diffusion model for photorealistic video generation. Our model is a transformer trained on image and video generation in a shared latent space. 🧵👇

Agrim Gupta

5,545 subscribers

431,142 Aufrufe • vor 2 Jahren •via X (Twitter)

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10 Kommentare

Profilbild von Agrim Gupta

Agrim Guptavor 2 Jahren

2/ website: Our approach has two key design decisions. First, we use a causal encoder to compress images and videos in a shared latent space.

Profilbild von Agrim Gupta

Agrim Guptavor 2 Jahren

3/ Second, for memory and training efficiency, we use a window attention based transformer architecture for joint spatial and temporal generative modeling in latent space.

Profilbild von Agrim Gupta

Agrim Guptavor 2 Jahren

4/ Our model can generate photorealistic, temporally consistent motion from natural language prompts.

Profilbild von Agrim Gupta

Agrim Guptavor 2 Jahren

5/ We can also use our model to animate any image.

Profilbild von Agrim Gupta

Agrim Guptavor 2 Jahren

6/ Finally, our model can be used to generate videos with consistent 3D camera motion.

Profilbild von Agrim Gupta

Agrim Guptavor 2 Jahren

7/ This work was done at @StanfordAILab, @StanfordSVL, @GoogleAI, @Google with amazing collaborators @LijunYu0, @kihyuk_sohn, @laoreja001, @MeeraHahn, @drfeifei, @irrfaan, @roadjiang, @jlezama

Profilbild von TomLikesRobots🤖

TomLikesRobots🤖vor 2 Jahren

Results look great - coherent and not much warping. Can inference run on consumer hardware? Will the code and weights be released?

Profilbild von Emad

Emadvor 2 Jahren

Great job, should scale very nicely 👀

Profilbild von Dave Lalande

Dave Lalandevor 2 Jahren

The end of Hollywood. It can't come fast enough.

Profilbild von Justin Halford

Justin Halfordvor 2 Jahren

Really incredible coherency. The scale to minutes of video and pairing with audio seems quite believable with leaps like this. Kudos to your team.

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