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Found 4 papers, 2 papers with code
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation
Vanilla text-to-image diffusion models struggle with generating accurate human images, commonly resulting in imperfect anatomies such as unnatural postures or disproportionate limbs. Existing methods address this issue mostly by fine-tuning the model with extra images or adding additional controls -- human-centric priors such as pose or depth maps -- during the image generation phase.
ITI-GEN: Inclusive Text-to-Image Generation
We show that, for some attributes, images can represent concepts more expressively than text.
Controllable 3D Generative Adversarial Face Model via Disentangling Shape and Appearance
3D face modeling has been an active area of research in computer vision and computer graphics, fueling applications ranging from facial expression transfer in virtual avatars to synthetic data generation.
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss.
