EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis
Sheng Miao, Sijin Li, Pan Wang, Dongfeng Bai, Bingbing Liu, Yue Wang, @andreasgeiger.bsky.social, @yiyiliao.bsky.social
tl;dr: EVolSplat journal version->dynamic scene
arxiv.org/abs/2601.15951
I am so happy and excited that this project got funded!
Scholar Inbox in conference mode is soooo useful. Get your poster session recommendations with the poster number and access the paper....
Thanks @andreasgeiger.bsky.social and team!
Attending #Neurips2025? Get your personalized Scholar Inbox conference program now to easily navigate the poster sessions and find what you are looking for:
www.scholar-inbox.com/conference/n...
🚀 New paper: ConeGS Error-Guided Densification Using Pixel Cones. We improve 3D Gaussian Splatting by placing Gaussians where they matter most: ConeGS adds primitives along pixel-view cones guided by image error, boosting quality with fewer Gaussians. baranowskibrt.github.io/conegs/
Congratulations to our PhD student @takerumiyato.bsky.social for winning the Google PhD Fellowship in the category "Machine Learning and ML Foundations". Takeru is pioneering new neural architectures that improve generalization and efficiency. Check out his research: takerum.github.io
🚀Excited to share our recent work on test-time scaling for feed-forward Gaussian splatting:
we learn a recurrent model ReSplat that is able to iteratively improve the reconstruction quality in a feed-forward manner!
haofeixu.github.io/resplat/
Personal programs for ICCV 2025 are now available at:
www.scholar-inbox.com/conference/i...
#TTT3R: 3D Reconstruction as Test-Time Training
TTT3R offers a simple state update rule to enhance length generalization for #CUT3R — No fine-tuning required!
🔗Page: rover-xingyu.github.io/TTT3R
We rebuilt @taylorswift13’s "22" live at the 2013 Billboard Music Awards - in 3D!
Video
Video
Video
PhD student at University of Tübingen working on artificial intelligence, machine learning, and deep learning research.
📢 We release "Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective" — a comprehensive survey covering 200+ papers on feed-forward 3D reconstruction!
Instead of categorizing by 3D representations, we propose a problem-driven taxonomy.
🌐 ff3d-survey.github.io
We are so proud of our faculty member @andreasgeiger.bsky.social for securing an ERC Consolidator Grant! With CASIDO, his team will design human-centric AI assistants to support researchers in the face of ever more rapid scientific progress.
@unituebingen.bsky.social @ml4science.bsky.social
