29 citations · 47 across the 5 of their papers we have counts for
5 papers
CHEF: Cross-modal Hierarchical Embeddings for Food Domain Retrieval
Hai X. Pham, Ricardo Guerrero, Jiatong Li +1
Despite the abundance of multi-modal data, such as image-text pairs, there has been little effort in understanding the individual entities and their different roles in the construc…
Cross-Modal Retrieval and Synthesis (X-MRS): Closing the Modality Gap in Shared Representation Learning
Ricardo Guerrero, Hai Xuan Pham, Vladimir Pavlovic
Computational food analysis (CFA) naturally requires multi-modal evidence of a particular food, e.g., images, recipe text, etc. A key to making CFA possible is multi-modal shared r…
Generative Adversarial Talking Head: Bringing Portraits to Life with a Weakly Supervised Neural Network
Hai X. Pham, Yuting Wang, Vladimir Pavlovic
This paper presents Generative Adversarial Talking Head (GATH), a novel deep generative neural network that enables fully automatic facial expression synthesis of an arbitrary port…
End-to-end Learning for 3D Facial Animation from Raw Waveforms of Speech
Hai X. Pham, Yuting Wang, Vladimir Pavlovic
We present a deep learning framework for real-time speech-driven 3D facial animation from just raw waveforms. Our deep neural network directly maps an input sequence of speech audi…
Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes
Hai X. Pham, Chongyu Chen, Luc N. Dao +3
We introduce a novel robust hybrid 3D face tracking framework from RGBD video streams, which is capable of tracking head pose and facial actions without pre-calibration or interven…