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Alessio Tonioni

15 accepted papers

2026

MOSAIC-GS: Monocular Scene Reconstruction via Advanced Initialization for Complex Dynamic Environments

Svitlana Morkva, Vaishakh Patil, Alessio Tonioni, Michael Oechsle, Maximum Wilder-Smith, Marco Hutter
CVPR 2026

We present MOSAIC-GS, a novel, fully explicit, and computationally efficient approach for high-fidelity dynamic scene reconstruction from monocular videos using Gaussian Splatting.Monocular reconstruction is inherently ill-posed due to the lack of sufficient multiview constraints, making accurate re

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2026

Shifting the Breaking Point of Flow Matching for Multi-Instance Editing

Carmine Zaccagnino, Fabio Quattrini, Enis Simsar, Marta Gazulla, Rita Cucchiara, Alessio Tonioni +1
ICML 2026poster

Flow matching models have recently emerged as an efficient alternative to diffusion, especially for text-guided image generation and editing, offering faster inference through continuous-time dynamics. However, existing flow-based editors predominantly support global or single-instruction edits and …

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2025

Active Data Curation Effectively Distills Large-Scale Multimodal Models

Vishaal Udandarao, Nikhil Parthasarathy, Muhammad Ferjad Naeem, Talfan Evans, Samuel Albanie, Federico Tombari +3
CVPR 2025poster

Knowledge distillation (KD) is the de facto standard for compressing large-scale models into smaller ones. Prior works have explored ever more complex KD strategies involving different objective functions, teacher-ensembles, and weight inheritance. In this work we explore an alternative, yet simple…

Cited by 6Source ↗PDF ↗Scholar ↗
2025

Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos

Chiara Plizzari, Alessio Tonioni, Yongqin Xian, Achin Kulshrestha, Federico Tombari
CVPR 2025poster

Understanding fine-grained temporal dynamics is crucial in egocentric videos, where continuous streams capture frequent, close-up interactions with objects. In this work, we bring to light that current egocentric video question-answering datasets often include questions that can be answered using on…

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2025

Test-Time Visual In-Context Tuning

Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr, Federico Tombari, Bernt Schiele
CVPR 2025poster

Visual in-context learning (VICL), as a new paradigm in computer vision, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. While effective, the existing VICL paradigm exhibits poor generalizability under distribution shifts. In this work, we propose test…

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2025

Training-free Online Video Step Grounding

Luca Zanella, Massimiliano Mancini, Yiming Wang, Alessio Tonioni, Elisa Ricci
NeurIPS 2025poster

Given a task and a set of steps composing it, Video Step Grounding (VSG) aims to detect which steps are performed in a video. Standard approaches for this task require a labeled training set (e.g., with step-level annotations or narrations), which may be costly to collect. Moreover, they process the…

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2025

UIP2P: Unsupervised Instruction-based Image Editing via Edit Reversibility Constraint

Enis Simsar, Alessio Tonioni, Yongqin Xian, Thomas Hofmann, Federico Tombari
ICCV 2025poster

We propose an unsupervised instruction-based image editing approach that removes the need for ground-truth edited images during training. Existing methods rely on supervised learning with triplets of input images, ground-truth edited images, and edit instructions. These triplets are typically genera…

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2025

Zero-Shot Styled Text Image Generation, but Make It Autoregressive

Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, Alessio Tonioni, Rita Cucchiara
CVPR 2025poster

Styled Handwritten Text Generation (HTG) has recently received attention from the computer vision and document analysis communities, which have developed several solutions, either GAN- or diffusion-based, that achieved promising results. Nonetheless, these strategies fail to generalize to novel styl…

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2024

Text-Conditioned Resampler For Long Form Video Understanding

Bruno Korbar*, Yongqin Xian, Alessio Tonioni, Andrew Zisserman, Federico Tombari
ECCV 2024poster

"In this paper we present a text-conditioned video resampler (TCR) module that uses a pre-trained and frozen visual encoder and large language model (LLM) to process long video sequences for a task. TCR localises relevant visual features from the video given a text condition and provides them to a L…

Cited by 12Source ↗PDF ↗Scholar ↗
2024

TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction Using Vision-Based Tactile Sensing

Mauro Comi, Yijiong Lin, Alex Church, Alessio Tonioni, Laurence Aitchison, Nathan F. Lepora
RA-L 2024

Humans rely on their visual and tactile senses to develop a comprehensive 3D understanding of their physical environment. Recently, there has been a growing interest in exploring and manipulating objects using data-driven approaches that utilise high-resolution vision-based tactile sensors. However,

Cited by 33Source ↗code ↗Scholar ↗
2019

Learning to Adapt for Stereo

Alessio Tonioni, Oscar Rahnama, Thomas Joy, Luigi Di Stefano, Thalaiyasingam Ajanthan, Philip H.S. Torr
CVPR 2019poster

Real world applications of stereo depth estimation require models that are robust to dynamic variations in the environment. Even though deep learning based stereo methods are successful, they often fail to generalize to unseen variations in the environment, making them less suitable for practical ap…

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