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Marc-André Carbonneau

6 accepted papers

2025

SEREP: Semantic Facial Expression Representation for Robust In-the-Wild Capture and Retargeting

ICCV 2025poster

Monocular facial performance capture in-the-wild is challenging due to varied capture conditions, face shapes, and expressions. Most current methods rely on linear 3D Morphable Models, which represent facial expressions independently of identity at the vertex displacement level. We propose SEREP (Se…

Cited by 0SourcePDFScholar
2024

BinaryAlign: Word Alignment as Binary Sequence Labeling

ACL 2024long

Real world deployments of word alignment are almost certain to cover both high and low resource languages. However, the state-of-the-art for this task recommends a different model class depending on the availability of gold alignment training data for a particular language pair. We propose BinaryAli…

2024

MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading

CVPR 2024poster

Reconstructing an avatar from a portrait image has many applications in multimedia but remains a challenging research problem. Extracting reflectance maps and geometry from one image is ill-posed: recovering geometry is a one-to-many mapping problem and reflectance and light are difficult to disenta…

Cited by 0SourcePDFScholar
2024

UPose3D: Uncertainty-Aware 3D Human Pose Estimation with Cross-View and Temporal Cues

ECCV 2024poster

"We introduce UPose3D, a novel approach for multi-view 3D human pose estimation, addressing challenges in accuracy and scalability. Our method advances existing pose estimation frameworks by improving robustness and flexibility without requiring direct 3D annotations. At the core of our method, a po…

2024

Zero-shot Cross-Lingual Transfer for Synthetic Data Generation in Grammatical Error Detection

EMNLP 2024main

Grammatical Error Detection (GED) methods rely heavily on human annotated error corpora. However, these annotations are unavailable in many low-resource languages. In this paper, we investigate GED in this context. Leveraging the zero-shot cross-lingual transfer capabilities of multilingual pre-trai…

2022

A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion

ICASSP 2022accepted

The goal of voice conversion is to transform source speech into a target voice, keeping the content unchanged. In this paper, we focus on self-supervised representation learning for voice conversion. Specifically, we compare discrete and soft speech units as input features. We find that discrete rep…

Cited by 0SourceScholar