Ruihang Zhang

incoming cs phd @uoft | ex-intern @snap

[Ruihang Zhang @ruihangzhang]

Introducing 📢📢ProxyPose📢📢

✨Track 6-DoF motion of any query pixel via video-to-video translation

📰ArXiv: https://t.co/yxC0E9cebN
💻Code: https://t.co/8IwtNv9oYq
🌐Webpage: https://t.co/KjpRMfEXxM

[Ruihang Zhang @ruihangzhang]

[4/4] Shoutout to the amazing team: @taubnerfelix , @pooja_ravioli, @kyroskutulakos, and @DaveLindell!

[Ruihang Zhang @ruihangzhang]

Introducing 📢📢ProxyPose📢📢

✨Track 6-DoF motion of any query pixel via video-to-video translation

📰ArXiv: https://t.co/yxC0E9cebN
💻Code: https://t.co/8IwtNv9oYq
🌐Webpage: https://t.co/KjpRMfEXxM

[Ruihang Zhang @ruihangzhang]

[3/4] 🚀Despite training only on synthetic data, ProxyPose outperforms previous work, especially on challenging cases like occlusion, reflective or transparent objects, textureless surfaces, and non-rigid motion—scenarios where many existing methods struggle.

[Ruihang Zhang @ruihangzhang]

@maxwell54650346 🔥

[Ruihang Zhang @ruihangzhang]

@HaojunQiu congrats @HaojunQiu!! amazing work!!

[Haojun Qiu @HaojunQiu]

📢📢📢We introduce Efficient-SID⚡️: training-free single-image diffusion model that generates images by sampling directly from an input image's patch distribution. Our method enables megapixel generation in <1s and scales to gigapixel generation. We also enable stylization, editing, and other applications. The outputs are constrained to follow exactly the patch distribution of the input — something that is very difficult to do with large models!

#CVPR2026 Highlight

🌐 https://t.co/HyPtTbmfvS
📄 https://t.co/caXEAk2wEQ

[1/6]

[Ruihang Zhang @ruihangzhang]

@yusuf_dalva wow big dawg 🫵

[Yusuf Dalva @yusuf_dalva]

Thrilled to announce that Canvas-to-Image (C2I) is accepted to #SIGGRAPH2026! 🎥

We simplify complex generation into one intuitive interface. Control ID, Pose, and Boxes: all from a single canvas. You are the designer.

Kudos to the team: @guocheng_qian, Maya Goldenberg, @tsaishien_chen, @AbermanKfir, @SergeyTulyakov, @PINguAR and @kcjacksonwang!

See you in LA!