Chih-Hao Lin

We’re presenting WeatherWeaver at #ICCV2025, Poster Session 3 (Oct 22, Wed, 10:45–12:45)!
Come visit #337 and see how we make it snow in Hawaii 🏝️❄️⛄

Finally, meet your #3DV2026 Publicity Chairs! 📢
@hanwenjiang1 @yanxg.bsky.social @chih-hao.bsky.social @csprofkgd.bsky.social

We’ll keep the 3DV conversation alive: posting updates, refreshing the website, and listening to your feedback.

Got questions or ideas? Tag @3dvconf.bsky.social anytime!

Introducing your #3DV2026 📝Publication Chairs &🔍Research Interaction Chairs!

📝Publication Chairs ensure accepted papers are properly published in the conference proceedings

🔍Research Interaction Chairs encourage engagement by spotlighting exceptional research in 3D vision

Understanding and reconstructing the 3D world are at the heart of computer vision and graphics. At #CVPR2025, we’ve seen many exciting works in 3D vision.
If you're pushing the boundaries, please consider submitting your work to #3DV2026 in Vancouver! (Deadline: Aug. 18, 2025)

This project was part of my internship at Meta, and it was a great collaboration with Jia-Bin, Zhengqin, Zhao, Christian, Tuotuo, Michael, Johannes, Shenlong, and Changil 🙌

📍 Meet us at #CVPR2025
🗓️ June 13 (Fri.), 10:30–12:30
🪧 Come by our poster session to chat about IRIS and any research ideas
Looking forward to reconnecting — and meeting new friends — in Nashville! 🎸✨

Please check out our paper, code, and more demo videos!
🌐 Project page: irisldr.github.io
💻 GitHub: github.com/facebookrese...
📝 Paper: arxiv.org/abs/2401.12977

Excited to share our work at #CVPR2025!
👁️IRIS estimates accurate surface material, spatially-varying HDR lighting, and camera response function given a set of LDR images! It enables realistic, view-consistent, and controllable relighting and object insertion.
(links in 🧵)

I’m thrilled to share that I will be joining Johns Hopkins University’s Department of Computer Science (@jhucompsci.bsky.social, @hopkinsdsai.bsky.social) as an Assistant Professor this fall.

Photo of Vancouver

📢 3DV 2026 – Call for Papers is Out!

📝 Paper Deadline: Aug 18
🎥 Supplementary: Aug 21
🔗 3dvconf.github.io/2026/call-fo...

📅 Conference Date: Mar 20–23, 2026
🌆 Location: Vancouver 🇨🇦

🚀 Showcase your latest research to the world!
#3DV2026 #CallForPapers #Vancouver #Canada

🔊 New NVIDIA paper: Audio-SDS 🔊
We repurpose Score Distillation Sampling (SDS) for audio, turning any pretrained audio diffusion model into a tool for diverse tasks, including source separation, impact synthesis & more.

🎧 Demos, audio examples, paper: research.nvidia.com/labs/toronto...

🧵below

This work is a great collaboration at NVIDIAAI by Chih-Hao Lin, Zian Wang, Ruofan Liang, Yuxuan Zhang, Sanja Fidler, Shenlong Wang, Zan Gojcic

🌐Please check out our project page: research.nvidia.com/labs/toronto...

The weather removal model successfully removes both transient (e.g., rain, snowflake) and persistent effects (e.g., puddle, snow cover), and can even restore sunny-day lighting from rainy/snowy videos.

WeatherWeaver combines two video diffusion models. The weather synthesis model generates realistic, temporally consistent weather, adapting shading naturally while preserving the original scene structure.

By combining and adjusting multiple weather effects, WeatherWeaver can simulate complex weather transitions, e.g. on 🌧️ rainy and ☃️ snowy days — without costly real-world acquisitions.

WeatherWeaver enables precise control of the weather effects by changing the intensity of the corresponding effects. 🌤️➡️🌥️

We train a video diffusion model to edit weather effects with precise control, using a novel data strategy combining synthetic videos, generative image editing, and auto-labeled real-world videos.

Realistic, controllable weather simulation opens new possibilities in 🚗 autonomous driving simulation and 🎬 filmmaking. Physics-based simulation requires accurate geometry and doesn’t scale to in-the-wild videos, while existing video editing often lacks realism and control.

What if you could control the weather in any video — just like applying a filter?
Meet WeatherWeaver, a video model for controllable synthesis and removal of diverse weather effects — such as 🌧️ rain, ☃️ snow, 🌁 fog, and ☁️ clouds — for any input video.

[1/10] Is scene understanding solved?

Models today can label pixels and detect objects with high accuracy. But does that mean they truly understand scenes?

Super excited to share our new paper and a new task in computer vision: Visual Jenga!

📄 arxiv.org/abs/2503.21770
🔗 visualjenga.github.io

Check out our cool demos. The code is also open-source!
Project website: haoyuhsu.github.io/autovfx-webs...
Code (GitHub): github.com/haoyuhsu/aut...
Paper: arxiv.org/abs/2411.02394

🎬Imagine creating professional visual effects (VFX) with just words! We are excited to introduce AutoVFX, a framework that creates realistic video effects from natural language instructions!

This is a cool project led by Hao-Yu, and we will present it at #3DV 2025!

Can we create realistic renderings of urban scenes from a single video while enabling controllable editing: relighting, object compositing, and nighttime simulation?

Check out our #3DV2025 UrbanIR paper, led by @chih-hao.bsky.social that does exactly this.

🔗: urbaninverserendering.github.io

Check out UrbanIR - Inverse rendering of unbounded scenes from a single video!

It’s a super cool project led by the amazing Chih-Hao!

@chih-hao.bsky.social is a rising star in 3DV! Follow him!

Learn more here👇

🙏 Huge thanks to our amazing collaborators from UIUC & UMD: Bohan, Yi-Ting, Kuan-Sheng, David, Jia-Bin (@jbhuang0604.bsky.social) , Anand (@anandbhattad.bsky.social) , and Shenlong. This work wouldn't be possible without you all.

📢 Meet us at 3DV 2025 in Singapore!
We’re excited to present UrbanIR at 3DV 2025 @3dvconf.bsky.social , come chat with us to discuss future directions!

Check out our interactive demos, and the code is open-source!
Project website: urbaninverserendering.github.io
Code (GitHub): github.com/chih-hao-lin...
Paper: arxiv.org/abs/2306.09349

UrbanIR precisely controls the lighting, simulating different times of day without time-consuming tripod captures.

With estimated scene properties, UrbanIR integrates a physically-based shading model into neural field, rendering realistic videos from novel viewpoints and lighting conditions.

🔍 How does it work?
UrbanIR reconstructs scene properties—geometry, albedo, and shading—through inverse rendering. Since this is a highly ill-posed problem, we leverage 2D priors to guide the optimization.