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Found 6 papers, 2 papers with code
Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance
Inspired by the effectiveness of 3D Gaussian Splatting (3DGS) in reconstructing detailed 3D scenes within multi-view setups and the emergence of large 2D human foundation models, we introduce Arc2Avatar, the first SDS-based method utilizing a human face foundation model as guidance with just a single image as input.
AnimateMe: 4D Facial Expressions via Diffusion Models
Recent advances in diffusion models have notably enhanced the capabilities of generative models in 2D animation.
Arc2Face: A Foundation Model for ID-Consistent Human Faces
This paper presents Arc2Face, an identity-conditioned face foundation model, which, given the ArcFace embedding of a person, can generate diverse photo-realistic images with an unparalleled degree of face similarity than existing models.
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ILSH: The Imperial Light-Stage Head Dataset for Human Head View Synthesis
This paper introduces the Imperial Light-Stage Head (ILSH) dataset, a novel light-stage-captured human head dataset designed to support view synthesis academic challenges for human heads.
Relightify: Relightable 3D Faces from a Single Image via Diffusion Models
Following the remarkable success of diffusion models on image generation, recent works have also demonstrated their impressive ability to address a number of inverse problems in an unsupervised way, by properly constraining the sampling process based on a conditioning input.
Neural Emotion Director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos
In this paper, we introduce a novel deep learning method for photo-realistic manipulation of the emotional state of actors in "in-the-wild" videos.
