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Yue Chen

@fanegg.bsky.social

81 followers 162 following 11 posts

PhD Student at Westlake University. 3D/4D Reconstruction, Human Intelligence. fanegg.github.io

Posts Replies Media

Yue Chen @fanegg.bsky.social · 08/10/2025

Code, model and 4D interactive demo now available 🔗Page: fanegg.github.io/Human3R 📄Paper: arxiv.org/abs/2510.06219 💻Code: github.com/fanegg/Human3R Big thanks to our awesome team! @fanegg.bsky.social @xingyu-chen.bsky.social Yuxuan Xue @apchen.bsky.social @xiuyuliang.bsky.social Gerard Pons-Moll

github.com

GitHub - fanegg/Human3R: An unified model for 4D human-scene reconstruction

An unified model for 4D human-scene reconstruction - fanegg/Human3R

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Yue Chen @fanegg.bsky.social · 08/10/2025

GT comparison shows our feedforward method, without any iterative optimization, is not only fast but also accurate. This is achieved by reading out humans from a 4D foundation model, #CUT3R, with our proposed 𝙝𝙪𝙢𝙖𝙣 𝙥𝙧𝙤𝙢𝙥𝙩 𝙩𝙪𝙣𝙞𝙣𝙜.

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Yue Chen @fanegg.bsky.social · 08/10/2025

Bonus: #Human3R is also a compact human tokenizer! Our human tokens capture ID+ shape + pose + position of human, unlocking 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴-𝗳𝗿𝗲𝗲 4D tracking.

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Yue Chen @fanegg.bsky.social · 08/10/2025

#Human3R: Everyone Everywhere All at Once Just input a RGB video, we online reconstruct 4D humans and scene in 𝗢𝗻𝗲 model and 𝗢𝗻𝗲 stage. Training this versatile model is easier than you think – it just takes 𝗢𝗻𝗲 day using 𝗢𝗻𝗲 GPU! 🔗Page: fanegg.github.io/Human3R/

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Yue Chen @fanegg.bsky.social · 01/10/2025

Again, training-free is all you need.

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Reposted by Yue Chen

Haiwen Huang @haiwen-huang.bsky.social · 22/04/2025

Excited to introduce LoftUp! A strong (than ever) and lightweight feature upsampler for vision encoders that can boost performance on dense prediction tasks by 20%–100%! Easy to plug into models like DINOv2, CLIP, SigLIP — simple design, big gains. Try it out! github.com/andrehuang/l...

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Reposted by Yue Chen

Andreas Geiger @andreasgeiger.bsky.social · 01/04/2025

I was really surprised when I saw this. Dust3R has learned very well to segment objects without supervision. This knowledge can be extracted post-hoc, enabling accurate 4D reconstruction instantly.

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Yue Chen @fanegg.bsky.social · 01/04/2025

Just "dissect" the cross-attention mechanism of #DUSt3R, making 4D reconstruction easier.

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Yue Chen @fanegg.bsky.social · 01/04/2025

#Easi3R is a simple training-free approach adapting DUSt3R for dynamic scenes.

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Yue Chen @fanegg.bsky.social · 31/03/2025

💻Code: github.com/fanegg/Feat2GS 🎥Video: youtu.be/4fT5lzcAJqo?... Big thanks to the amazing team! @fanegg.bsky.social, @xingyu-chen.bsky.social, Anpei Chen, Gerard Pons-Moll, Yuliang Xiu #DUSt3R #MASt3R #MiDaS #DINOv2 #DINO #SAM #CLIP #RADIO #MAE #StableDiffusion #Zero123

youtu.be

[CVPR 2025] Feat2GS: Probing Visual Foundation Models with Gaussian Splatting

YouTube video by Yue Chen

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Yue Chen @fanegg.bsky.social · 31/03/2025

Our findings in 3D probe lead to a simple-yet-effective solution, by just combining features from different visual foundation models and outperform prior works. Apply #Feat2GS in sparse & causal captures: 🤗Online Demo: huggingface.co/spaces/endle...

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Yue Chen @fanegg.bsky.social · 31/03/2025

With #Feat2GS we evaluated more than 10 visual foundation models (DUSt3R, DINO, MAE, SAM, CLIP, MiDas, etc) in terms of geometry and texture — see the paper for comparison. 📄Paper: arxiv.org/abs/2412.09606 🔍Try it NOW: fanegg.github.io/Feat2GS/#chart

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Yue Chen @fanegg.bsky.social · 31/03/2025

How much 3D do visual foundation models (VFMs) know? Previous work requires 3D data for probing → expensive to collect! #Feat2GS @cvprconference.bsky.social 2025 - our idea is to read out 3D Gaussains from VFMs features, thus probe 3D with novel view synthesis. 🔗Page: fanegg.github.io/Feat2GS

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