Hu, Cheng, Yu et al., "VGGT4D: Mining Motion Cues in Visual Geometry Transformers for 4D Scene Reconstruction" Easi3r-style attention analysis and masking with mask refinement with VGGT. Also discards tokens related to dynamic points.

Personal programs for ICCV 2025 are now available at: www.scholar-inbox.com/conference/i...

Look, 4D foundation models know about humans – and we just read it out!

Glad to be recognized as an outstanding reviewer!

#VGGT: accurate within short clips, but slow and prone to Out-of-Memory (OOM) #CUT3R: fast with constant memory usage, but forgets. We revisit them from a Test-Time Training (TTT) perspective and propose #TTT3R to get all three: fast, accurate, and OOM-free.

Let's keep revisiting 3D reconstruction!

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...

If you're a researcher and haven't tried it yet, please give it a try! It took me a while to adjust, but now it's my favorite tool. You can read, bookmark, organize papers, and get recommendations based on your interests!

Easi3R: Estimating Disentangled Motion from DUSt3R Without Training @xingyu-chen.bsky.social, @fanegg.bsky.social, @xiuyuliang.bsky.social, @andreasgeiger.bsky.social, @apchen.bsky.social arxiv.org/abs/2503.24391

𝗘𝗮𝘀𝗶𝟯𝗥: 𝗘𝘀𝘁𝗶𝗺𝗮𝘁𝗶𝗻𝗴 𝗗𝗶𝘀𝗲𝗻𝘁𝗮𝗻𝗴𝗹𝗲𝗱 𝗠𝗼𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗗𝗨𝗦𝘁𝟯𝗥 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 Xingyu Chen, Yue Chen, Yuliang Xiu ... Anpei Chen arxiv.org/abs/2503.24391 Trending on www.scholar-inbox.com

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.

🦣Easi3R: 4D Reconstruction Without Training! Limited 4D datasets? Take it easy. #Easi3R adapts #DUSt3R for 4D reconstruction by disentangling and repurposing its attention maps → make 4D reconstruction easier than ever! 🔗Page: easi3r.github.io

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