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Nikos Athanasiou

7 accepted papers

2025

Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing

Maria-Paola Forte, Nikos Athanasiou, Giulia Ballardini, Jan Ulrich Bartels, Katherine J. Kuchenbecker, Michael J. Black
ICCV 2025poster

Capturing accurate 3D human pose in the wild would provide valuable data for training motion-generation and pose-estimation methods. While video-based estimation approaches have become increasingly accurate, they often fail in common scenarios involving self-contact, such as a hand touching the face…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Emotional Speech-driven 3D Body Animation via Disentangled Latent Diffusion

Kiran Chhatre, Radek Dan??ek, Nikos Athanasiou, Giorgio Becherini, Christopher Peters, Michael J. Black +1
CVPR 2024poster

Existing methods for synthesizing 3D human gestures from speech have shown promising results but they do not explicitly model the impact of emotions on the generated gestures. Instead these methods directly output animations from speech without control over the expressed emotion. To address this lim…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2024

WANDR: Intention-guided Human Motion Generation

Markos Diomataris, Nikos Athanasiou, Omid Taheri, Xi Wang, Otmar Hilliges, Michael J. Black
CVPR 2024poster

Synthesizing natural human motions that enable a 3D human avatar to walk and reach for arbitrary goals in 3D space remains an unsolved problem with many applications. Existing methods (data-driven or using reinforcement learning) are limited in terms of generalization and motion naturalness. A prima…

Cited by 12Source ↗PDF ↗Scholar ↗
2023

SINC: Spatial Composition of 3D Human Motions for Simultaneous Action Generation

Nikos Athanasiou, Mathis Petrovich, Michael J. Black, Gül Varol
ICCV 2023poster

Our goal is to synthesize 3D human motions given textual inputs describing simultaneous actions, for example `waving hand' while `walking' at the same time. We refer to generating such simultaneous movements as performing `spatial compositions'. In contrast to `temporal compositions' that seek to tr…

Cited by 48PDF ↗Scholar ↗
2021

BABEL: Bodies, Action and Behavior With English Labels

Abhinanda R. Punnakkal, Arjun Chandrasekaran, Nikos Athanasiou, Alejandra Quiros-Ramirez, Michael J. Black
CVPR 2021poster

Understanding the semantics of human movement -- the what, how and why of the movement -- is an important problem that requires datasets of human actions with semantic labels. Existing datasets take one of two approaches. Large-scale video datasets contain many action labels but do not contain groun…

Cited by 236PDF ↗code ↗Scholar ↗
2021

Learning To Regress Bodies From Images Using Differentiable Semantic Rendering

Sai Kumar Dwivedi, Nikos Athanasiou, Muhammed Kocabas, Michael J. Black
ICCV 2021poster

Learning to regress 3D human body shape and pose (e.g. SMPL parameters) from monocular images typically exploits losses on 2D keypoints, silhouettes, and/or part-segmentation when 3D training data is not available. Such losses, however, are limited because 2D keypoints do not supervise body shape an…

Cited by 65PDF ↗code ↗Scholar ↗
2020

VIBE: Video Inference for Human Body Pose and Shape Estimation

Muhammed Kocabas, Nikos Athanasiou, Michael J. Black
CVPR 2020poster

Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem…

Cited by 1216PDF ↗code ↗Scholar ↗