Ziyi Wu's banner
Ziyi Wu's profile picture

Ziyi Wu

@Dazitu_616 • 1,241 subscribers

Incoming RS @GoogleDeepMind working on Genie, CS PhD @UofT | Prev intern at @GoogleDeepMind, @Snap, Undergrad @Tsinghua_Uni

Videos

LIVE
1.2k

Anya Rossi

sweetdream.ai

SweetDream.aiSponsored•Livecam
Streaming Now

Watch Anya Live

Anya is streaming live right now! Join her private show and enjoy exclusive content.

HD live stream
Exclusive private shows
1.2k viewers online

Free preview available • Premium content

📢Introducing 360Anything, our method for lifting any perspective image or video to gravity-aligned 360° panoramas without using any camera or 3D information. This enables consistent novel view synthesis and 3D scene reconstruction. Project page: 🧵
1:05
Dazitu_616's profile picture

📢Introducing 360Anything, our method for lifting any perspective image or video to gravity-aligned 360° panoramas without using any camera or 3D information. This enables consistent novel view synthesis and 3D scene reconstruction. Project page: 🧵

Ziyi Wu

46,766 views • 4 months ago

*Why panorama?* Standard video models struggle with object permanence—if a camera pans away and comes back, objects may disappear. With panoramas, the model is forced to generate everything in the scene. This serves as a "working memory" for consistent world generation. (3/N)
0:50
Dazitu_616's profile picture

*Why panorama?* Standard video models struggle with object permanence—if a camera pans away and comes back, objects may disappear. With panoramas, the model is forced to generate everything in the scene. This serves as a "working memory" for consistent world generation. (3/N)

Ziyi Wu

21,992 views • 4 months ago

📢 Introducing DenseDPO: Fine-Grained Temporal Preference Optimization for Video Diffusion Models Compared to vanilla DPO, we improve paired data construction and preference label granularity, leading to better visual quality and motion strength with only 1/3 of the data. 🧵
0:48
Dazitu_616's profile picture

📢 Introducing DenseDPO: Fine-Grained Temporal Preference Optimization for Video Diffusion Models Compared to vanilla DPO, we improve paired data construction and preference label granularity, leading to better visual quality and motion strength with only 1/3 of the data. 🧵

Ziyi Wu

35,374 views • 1 year ago

No more content to load