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Ikhsanul Habibie

5 accepted papers

2024

ConvoFusion: Multi-Modal Conversational Diffusion for Co-Speech Gesture Synthesis

Muhammad Hamza Mughal, Rishabh Dabral, Ikhsanul Habibie, Lucia Donatelli, Marc Habermann, Christian Theobalt
CVPR 2024poster

Gestures play a key role in human communication. Recent methods for co-speech gesture generation while managing to generate beat-aligned motions struggle generating gestures that are semantically aligned with the utterance. Compared to beat gestures that align naturally to the audio signal semantica…

Cited by 12Source ↗PDF ↗Scholar ↗
2023

Imitator: Personalized Speech-driven 3D Facial Animation

Balamurugan Thambiraja, Ikhsanul Habibie, Sadegh Aliakbarian, Darren Cosker, Christian Theobalt, Justus Thies
ICCV 2023poster

Speech-driven 3D facial animation has been widely explored, with applications in gaming, character animation, virtual reality, and telepresence systems. State-of-the-art methods deform the face topology of the target actor to sync the input audio without considering the identity-specific speaking st…

Cited by 60PDF ↗code ↗Scholar ↗
2021

Monocular Real-Time Full Body Capture With Inter-Part Correlations

Yuxiao Zhou, Marc Habermann, Ikhsanul Habibie, Ayush Tewari, Christian Theobalt, Feng Xu
CVPR 2021poster

We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational e…

Cited by 72PDF ↗Scholar ↗
2020

Monocular Real-Time Hand Shape and Motion Capture Using Multi-Modal Data

Yuxiao Zhou, Marc Habermann, Weipeng Xu, Ikhsanul Habibie, Christian Theobalt, Feng Xu
CVPR 2020poster

We present a novel method for monocular hand shape and pose estimation at unprecedented runtime performance of 100fps and at state-of-the-art accuracy. This is enabled by a new learning based architecture designed such that it can make use of all the sources of available hand training data: image da…

Cited by 257PDF ↗code ↗Scholar ↗
2019

In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations

Ikhsanul Habibie, Weipeng Xu, Dushyant Mehta, Gerard Pons-Moll, Christian Theobalt
CVPR 2019oral

Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations…

Cited by 178PDF ↗Scholar ↗