24 citations · 62 across the 5 of their papers we have counts for
7 papers · 1 filter
Dimitra: Audio-driven Diffusion model for Expressive Talking Head Generation
Baptiste Chopin, Tashvik Dhamija, Pranav Balaji +2
We propose Dimitra, a novel framework for audio-driven talking head generation, streamlined to learn lip motion, facial expression, as well as head pose motion. Specifically, we tr…
Latent Image Animator: Learning to Animate Images via Latent Space Navigation
Yaohui Wang, Di Yang, Francois Bremond +1
Due to the remarkable progress of deep generative models, animating images has become increasingly efficient, whereas associated results have become increasingly realistic. Current…
UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition
Di Yang, Yaohui Wang, Antitza Dantcheva +3
Action recognition based on skeleton data has recently witnessed increasing attention and progress. State-of-the-art approaches adopting Graph Convolutional networks (GCNs) can eff…
InMoDeGAN: Interpretable Motion Decomposition Generative Adversarial Network for Video Generation
Yaohui Wang, Francois Bremond, Antitza Dantcheva
In this work, we introduce an unconditional video generative model, InMoDeGAN, targeted to (a) generate high quality videos, as well as to (b) allow for interpretation of the laten…
Joint Generative and Contrastive Learning for Unsupervised Person Re-identification
Hao Chen, Yaohui Wang, Benoit Lagadec +2
Recent self-supervised contrastive learning provides an effective approach for unsupervised person re-identification (ReID) by learning invariance from different views (transformed…
Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos
Di Yang, Rui Dai, Yaohui Wang +4
Taking advantage of human pose data for understanding human activities has attracted much attention these days. However, state-of-the-art pose estimators struggle in obtaining high…