Yiming Dou

Ph.D. student at Cornell | Computer Vision, Multimodal, Robotics

[Yiming Dou @_YimingDou]

Ever wondered how a scene sounds👂 when you interact👋 with it?

Introducing our #CVPR2025 work "Hearing Hands: Generating Sounds from Physical Interactions in 3D Scenes" -- we make 3D scene reconstructions audibly interactive!

https://t.co/tIcFGJtB7R

[Dandan Shan @DandanShan_]

🧐A question I've long been interested in: how can we learn from human hands and transfer that directly to robots?

Our new work, HUG, makes it possible in three simple steps: (1) collect human grasps at scale, (2) learn from them, and (3) retarget for deployment.

[Yiming Dou @_YimingDou]

@_crockwell Congrats Chris🚀

[Gene Chou @gene_ch0u]

Introducing CityRAG!

We wanted video generative models to be grounded in the real world — if I’m in London, I want to look around and actually see Big Ben.

CityRAG generates videos of cities featuring real buildings and roads, with arbitrary weather, people, and cars. 1/N

page: https://t.co/jxMSX5Ik7F
paper: https://t.co/So2V9hyB4D

[Paul Liang @pliang279]

Despite much progress in AI, the ability for AI to 'smell' like humans remains elusive. Smell AIs 🤖👃can be used for allergen sensing (e.g., peanuts or gluten in food), hormone detection for health, safety & environmental monitoring, quality control in manufacturing, and more.

As a step towards AI for smell, our group is releasing **SmellNet,** a massive open dataset to advance AI smell-recognition in real-world settings. Using portable gas and chemical sensors, we collected 180,000 time steps of 50 substances (spanning nuts, spices, herbs, fruits, and vegetables) with 50 hours of data.

SmellNet enables the training of AI models for real-time classification of substances based on their smell alone - see video below, where even subtle differences between cumin, cloves, and oregano can be detected.

Check out our paper and open-source data & code for the smell AI revolution!

paper: https://t.co/dMxPMwKiAe
data & code: https://t.co/B1NfIVYZqh

w Dewei, Carol, David @ddvd233 @medialab @MITEECS

[Linyi Jin @jin_linyi]

Hello! If you are interested in dynamic 3D or 4D, don't miss the oral session 3A at 9 am on Saturday:

@zhengqi_li
will be presenting "MegaSaM"
I'll be presenting "Stereo4D"
and
@QianqianWang5
will be presenting "CUT3R"

[Yiming Dou @_YimingDou]

@zizhpan Haha thanks���

[Yiming Dou @_YimingDou]

Ever wondered how a scene sounds👂 when you interact👋 with it?

Introducing our #CVPR2025 work "Hearing Hands: Generating Sounds from Physical Interactions in 3D Scenes" -- we make 3D scene reconstructions audibly interactive!

https://t.co/tIcFGJtB7R

[Ayush Shrivastava @ayshrv]

Excited to share our CVPR 2025 paper on cross-modal space-time correspondence!

We present a method to match pixels across different modalities (RGB-Depth, RGB-Thermal, Photo-Sketch, and cross-style images) — trained entirely using unpaired data and self-supervision.

Our approach learns correspondences through contrastive random walks across visual modalities.

#CVPR2025 (1/6)

[Jeongsoo Park @jespark0]

Can AI image detectors keep up with new fakes?

Mostly, no. Existing detectors are trained using a handful of models. But there are thousands in the wild!

Our work, Community Forensics, uses 4800+ generators to train detectors that generalize to new fakes.

#CVPR2025 🧵 (1/5)

[Yiming Dou @_YimingDou]

Wonderful collaboration with Wonseok Oh, Yuqing Luo, @antoniloq, @andrewhowens!!!

[Yiming Dou @_YimingDou]

Combining with our previous #CVPR2024 work TaRF (https://t.co/p2wzv927a6), we create an immersive 3D scene reconstruction that allows users to interact with it using sight👀, touch👆 and sound👂.

[Daniel Geng @dangengdg]

Hello! If you like pretty images and videos and want a rec for CVPR oral session, you should def go to Image/Video Gen, Friday at 9am:

I'll be presenting "Motion Prompting" @RyanBurgert will be presenting "Go with the Flow" and @ChangPasca1650 will be presenting "LookingGlass"

[Chris Rockwell @_crockwell]

Ever wish YouTube had 3D labels?

🚀Introducing🎥DynPose-100K🎥, an Internet-scale collection of diverse videos annotated with camera pose!

Applications include camera-controlled video generation🤩and learned dynamic pose estimation😯

Download: https://t.co/iL3iqqzYL8

[Yuanchen Ju @ju_yuanchen]

🧩#CVPR2025🌷Introducing Two By Two✌️: The First Large-Scale Daily Pairwise Assembly Dataset with SE(3)-Equivariant Pose Estimation.
🤖2BY2 helps robots master daily 3D assembly tasks—like plugging sockets or arranging flowers—across diverse objects!
🐨Co-lead by @yuqi_Beijing

[Yiming Dou @_YimingDou]

Thanks to @OpenAI, got a chance to grow up again in Ghibli anime🤗

[Sarah Jabbour @SarahJabbour_]

I’m on the PhD internship market for Spr/Summer 2025! I have experience in multimodal AI (EHR, X-ray, text), explainability for image models w/ genAI, clinician-AI interaction (surveyed 700+ doctors), and tabular foundation models. Please reach out if you think there’s a fit!

[Yuanchen Ju @ju_yuanchen]

🍌We present DenseMatcher！
🤖️DenseMatcher enables robots to acquire generalizable skills across diverse object categories by only seeing one demo, by finding correspondences between 3D objects even with different types, shapes, and appearances.