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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 görüntüleme • 2 yıl önce •via X (Twitter)

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Agrim Gupta profil fotoğrafı

Agrim Gupta2 yıl önce

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.

Agrim Gupta profil fotoğrafı

Agrim Gupta2 yıl önce

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.

Agrim Gupta profil fotoğrafı

Agrim Gupta2 yıl önce

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

Agrim Gupta profil fotoğrafı

Agrim Gupta2 yıl önce

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

Agrim Gupta profil fotoğrafı

Agrim Gupta2 yıl önce

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

Agrim Gupta profil fotoğrafı

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

TomLikesRobots🤖 profil fotoğrafı

TomLikesRobots🤖2 yıl önce

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

Emad profil fotoğrafı

Emad2 yıl önce

Great job, should scale very nicely 👀

Dave Lalande profil fotoğrafı

Dave Lalande2 yıl önce

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

Justin Halford profil fotoğrafı

Justin Halford2 yıl önce

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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