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๐Ÿ“ข๐Ÿ“ข๐Ÿ“ข ๐€๐‚๐Ÿ‘๐ƒ: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers TL;DR: for 3D camera control in generative video, it really helps knowing *which* part of your model you should mess with Internship by Sherwin Bahmani at Snap

23,040 views โ€ข 1 year ago โ€ขvia X (Twitter)

5 Comments

Andrea Tagliasacchi ๐Ÿ‡จ๐Ÿ‡ฆ's profile picture
Andrea Tagliasacchi ๐Ÿ‡จ๐Ÿ‡ฆ1 year ago

TL;DR (expanded): 1) "when" in the diffusion process you condition for camera matters (i.e. noise scheduler) 2) "how" in the diffusion process you condition for camera maters (i.e. architecture) 3) "what data" you give to your diffusion model to condition camera matters

Andrea Tagliasacchi ๐Ÿ‡จ๐Ÿ‡ฆ's profile picture
Andrea Tagliasacchi ๐Ÿ‡จ๐Ÿ‡ฆ1 year ago

Why, you ask? 1) camera motion is low-frequency... early denoising iterations deal with low-frequency content 2) early DiT blocks are enough to fine-tune for camera control... more and you lose quality 3) model needs to know what a static view of the dynamic world looks like

Andrea Tagliasacchi ๐Ÿ‡จ๐Ÿ‡ฆ's profile picture
Andrea Tagliasacchi ๐Ÿ‡จ๐Ÿ‡ฆ1 year ago

A shout to the collaborators @isskoro @guocheng_qian A. Siarohin @willimenapace @SergeyTulyakov at Snap and @DaveLindell at UofT.

Samarth Sinha's profile picture
Samarth Sinha1 year ago

@sherwinbahmani Congrats @sherwinbahmani !!

Abdullah Hamdi's profile picture
Abdullah Hamdi1 year ago

@sherwinbahmani Congrats to the team ! Amazing work

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