34 citations · 104 across the 13 of their papers we have counts for
15 papers · 1 filter
TrustCLIP: Learning Private Visual Features via Adversarial Reconstruction
Nikos Athanasiou, Ilya A. Petrov, Angela Yao +7
Vision and vision-language models rely on high-level visual representations that are increasingly used across recognition, retrieval, and multimodal reasoning pipelines. However, r…
PALM: A Dataset and Baseline for Learning Multi-subject Hand Prior
Zicong Fan, Edoardo Remelli, David Dimond +5
The ability to grasp objects, signal with gestures, and share emotion through touch all stem from the unique capabilities of human hands. Yet creating high-quality personalized han…
Memory-efficient Streaming VideoLLMs for Real-time Procedural Video Understanding
Dibyadip Chatterjee, Edoardo Remelli, Yale Song +9
We introduce ProVideLLM, an end-to-end framework for real-time procedural video understanding. ProVideLLM integrates a multimodal cache configured to store two types of tokens - ve…
X-MIC: Cross-Modal Instance Conditioning for Egocentric Action Generalization
Anna Kukleva, Fadime Sener, Edoardo Remelli +4
Lately, there has been growing interest in adapting vision-language models (VLMs) to image and third-person video classification due to their success in zero-shot recognition. Howe…
DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptions
Sammy Christen, Shreyas Hampali, Fadime Sener +5
Generating natural hand-object interactions in 3D is challenging as the resulting hand and object motions are expected to be physically plausible and semantically meaningful. Furth…
DIG: Draping Implicit Garment over the Human Body
Ren Li, Benoît Guillard, Edoardo Remelli +1
Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differen…
Drivable Volumetric Avatars using Texel-Aligned Features
Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito +8
Photorealistic telepresence requires both high-fidelity body modeling and faithful driving to enable dynamically synthesized appearance that is indistinguishable from reality. In t…
DeepMesh: Differentiable Iso-Surface Extraction
Benoit Guillard, Edoardo Remelli, Artem Lukoianov +4
Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary…
Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches
Benoit Guillard, Edoardo Remelli, Pierre Yvernay +1
Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information. In this paper, we use an encoder/dec…
Unsupervised Domain Adaptation with Temporal-Consistent Self-Training for 3D Hand-Object Joint Reconstruction
Mengshi Qi, Edoardo Remelli, Mathieu Salzmann +1
Deep learning-solutions for hand-object 3D pose and shape estimation are now very effective when an annotated dataset is available to train them to handle the scenarios and lightin…
UCLID-Net: Single View Reconstruction in Object Space
Benoit Guillard, Edoardo Remelli, Pascal Fua
Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit repr…
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter +4
Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary…
Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation
Edoardo Remelli, Shangchen Han, Sina Honari +2
We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras. Building upon recent advances in interpretable representatio…
Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data
Udaranga Wickramasinghe, Edoardo Remelli, Graham Knott +1
CNN-based volumetric methods that label individual voxels now dominate the field of biomedical segmentation. However, 3D surface representations are often required for proper analy…
NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler
Edoardo Remelli, Pierre Baque, Pascal Fua
Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points. Furthermore, they typically require…