Federico Tombari

109 posts

Federico Tombari

Federico Tombari

@fedassa

3D computer vision and ML for AR and robotics, @Google and TU Munich

Zürich Katılım Ekim 2012
190 Takip Edilen2.6K Takipçiler
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Michael Niemeyer
Michael Niemeyer@Mi_Niemeyer·
Hiring: Student Researcher @ Google Zurich! 🇨🇭 Looking for an EMEA-based PhD student to work on: Efficient 3DGS / Feedforward Models / 3D GenAI. Start: June 2026 or earlier. If interested, please reach out with your CV to: sr-zurich-3d@google.com
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Baráth Dániel
Baráth Dániel@majti89·
📢 Exciting news: The 1st GenRecon3D is coming to #CVPR2026! 🚀 We are exploring how to reconstruct more than what is directly visible using generative priors. Fantastic speaker lineup coming soon. 📍 Website: genrecon3d.github.io #CVPR #GenAI #3DVision
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Julia Grabinski
Julia Grabinski@JuliaGrabinski·
PhD: DONE! ✅ If you are wondering how I feel, just check out these smiling pics. Incredibly thankful and happy! 😊 #PhDLife #DoneAndDusted
Julia Grabinski tweet mediaJulia Grabinski tweet mediaJulia Grabinski tweet media
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Haiyang Wang
Haiyang Wang@haiyang73756134·
Excited to see our paper "Tokenformer: Rethinking transformer scaling with tokenized model parameters" accepted as a spotlight at #ICLR2025 ! Hope our idea of ​​tokenizing everything can inspire the future of AI. Paper: arxiv.org/abs/2410.23168 Code: github.com/Haiyang-W/Toke…
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Federico Tombari
Federico Tombari@fedassa·
Google Internship call: we are looking for a PhD student in the area of object/scene understanding and VLMs to join our Google team in Zurich next summer. If you have applied for the 2025 Google Internship Call already and are interested to know more, ping me!
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Enis Simsar
Enis Simsar@enisimsar·
🚀 Excited to introduce UIP2P: Unsupervised Instruction-Based Image Editing via Cycle Edit Consistency (CEC)! TL;DR: Scalable instruction-based image editing without paired image data. Works on real-world datasets with robust, reversible edits! Thanks to Alessio Tonioni, @xyongqin, @THofmann2017, @fedassa!
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MrNeRF
MrNeRF@janusch_patas·
SuperGSeg: Open-Vocabulary 3D Segmentation with Structured Super-Gaussians Contributions: • We propose SuperGSeg: a 3D segmentation method with neural Gaussians, designed to learn hierarchical instance segmentation features from 2D foundation models. • We introduce the concept of Super-Gaussian, a novel representation that integrates hierarchical instance segmentation features, enabling the embedding of high-dimensional language features. This approach addresses previously unfeasible challenges in representing complex scenes with rich semantic details. • Extensive experiments on the LERF-OVS and ScanNet datasets demonstrate the effectiveness of the proposed method, achieving significant improvements in open-vocabulary 3D object-level and scene-level semantic segmentation. It shows particular strength in capturing fine-grained scene details and dense pixel semantic segmentation tasks for the first time.
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Enis Simsar
Enis Simsar@enisimsar·
🚀 Excited to share our preprint LoRACLR! TL;DR: LoRACLR merges multiple LoRA models into a unified diffusion model for seamless, high-fidelity multi-concept image synthesis with minimal interference. Thanks to @THofmann2017, @fedassa, and @PINguAR! 🙌
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Nikhil Parthasarathy
Nikhil Parthasarathy@nikparth1·
Thrilled to share our latest work showing that “distillation through data” can be more effective than traditional knowledge-distillation (KD) for efficient multimodal pretraining- our distilled models have achieve SoTA performance at less inference cost! arxiv.org/abs/2411.18674
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