  Previous methods have dealt with discrete manipulation of facial attributes
such as smile, sad, angry, surprise etc, out of canonical expressions and they
are not scalable, operating in single modality. In this paper, we propose a
novel framework that supports continuous edits and multi-modality portrait
manipulation using adversarial learning. Specifically, we adapt
cycle-consistency into the conditional setting by leveraging additional facial
landmarks information. This has two effects: first cycle mapping induces
bidirectional manipulation and identity preserving; second pairing samples from
different modalities can thus be utilized. To ensure high-quality synthesis, we
adopt texture-loss that enforces texture consistency and multi-level
adversarial supervision that facilitates gradient flow. Quantitative and
qualitative experiments show the effectiveness of our framework in performing
flexible and multi-modality portrait manipulation with photo-realistic effects.
