Face generation is the task of generating (or interpolating) new faces from an existing dataset.
The state-of-the-art results for this task are located in the Image Generation parent.
|Trend||Dataset||Best Method||Paper title||Paper||Code||Compare|
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis.
We propose a novel end-to-end semi-supervised adversarial framework to generate photorealistic face images of new identities with wide ranges of expressions, poses, and illuminations conditioned by a 3D morphable model.
This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves.
To show this is effective, we incorporate the triple consistency loss into the training of a new landmark-guided face to face synthesis, where, contrary to previous works, the generated images can simultaneously undergo a large transformation in both expression and pose.