30 citations · 35 across the 8 of their papers we have counts for
9 papers
Efficiently Reconstructing Dynamic Scenes One D4RT at a Time
Chuhan Zhang, Guillaume Le Moing, Skanda Koppula +11
Understanding and reconstructing the complex geometry and motion of dynamic scenes from video remains a formidable challenge in computer vision. This paper introduces D4RT, a simpl…
SciVid: Cross-Domain Evaluation of Video Models in Scientific Applications
Yana Hasson, Pauline Luc, Liliane Momeni +10
In recent years, there has been a proliferation of spatiotemporal foundation models in different scientific disciplines. While promising, these models are often domain-specific and…
Scaling 4D Representations
João Carreira, Dilara Gokay, Michael King +32
Scaling has not yet been convincingly demonstrated for pure self-supervised learning from video. However, prior work has focused evaluations on semantic-related tasks $\unicode{x20…
Dense Optical Tracking: Connecting the Dots
Guillaume Le Moing, Jean Ponce, Cordelia Schmid
Recent approaches to point tracking are able to recover the trajectory of any scene point through a large portion of a video despite the presence of occlusions. They are, however,…
CCVS: Context-aware Controllable Video Synthesis
Guillaume Le Moing, Jean Ponce, Cordelia Schmid
This presentation introduces a self-supervised learning approach to the synthesis of new video clips from old ones, with several new key elements for improved spatial resolution an…
Semantic Palette: Guiding Scene Generation with Class Proportions
Guillaume Le Moing, Tuan-Hung Vu, Himalaya Jain +2
Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem. Previous…
Learning Multiple Sound Source 2D Localization
Guillaume Le Moing, Phongtharin Vinayavekhin, Tadanobu Inoue +4
In this paper, we propose novel deep learning based algorithms for multiple sound source localization. Specifically, we aim to find the 2D Cartesian coordinates of multiple sound s…
Ensemble of Discriminators for Domain Adaptation in Multiple Sound Source 2D Localization
Guillaume Le Moing, Don Joven Agravante, Tadanobu Inoue +4
This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources. Recently, deep neura…
Data-Efficient Framework for Real-world Multiple Sound Source 2D Localization
Guillaume Le Moing, Phongtharin Vinayavekhin, Don Joven Agravante +4
Deep neural networks have recently led to promising results for the task of multiple sound source localization. Yet, they require a lot of training data to cover a variety of acous…