85 citations · 267 across the 19 of their papers we have counts for
19 papers
AI killed the video star. 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…
THEval. Evaluation Framework for Talking Head Video Generation
Nabyl Quignon, Baptiste Chopin, Yaohui Wang +1
Video generation has achieved remarkable progress, with generated videos increasingly resembling real ones. However, the rapid advance in generation has outpaced the development of…
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…
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
Yaohui Wang, Xinyuan Chen, Xin Ma +17
This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challengi…
LAC: Latent Action Composition for Skeleton-based Action Segmentation
Di Yang, Yaohui Wang, Antitza Dantcheva +4
Skeleton-based action segmentation requires recognizing composable actions in untrimmed videos. Current approaches decouple this problem by first extracting local visual features f…
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Yi Wang, Yinan He, Yizhuo Li +13
This paper introduces InternVid, a large-scale video-centric multimodal dataset that enables learning powerful and transferable video-text representations for multimodal understand…