Accurate and controllable image editing is a challenging task that has attracted significant
attention recently. Notably DragGAN developed by Pan et al.(2023) is an interactive point …

Synthesizing visual content that meets users' needs often requires flexible and precise
controllability of the pose, shape, expression, and layout of the generated objects. Existing …

Diffusion models have recently become the de-facto approach for generative modeling in
the 2D domain. However, extending diffusion models to 3D is challenging, due to the …

Existing methods for 3D-aware image synthesis largely depend on the 3D pose distribution
pre-estimated on the training set. An inaccurate estimation may mislead the model into …

Recently we have witnessed the explosive growth of various volumetric representations in
modeling animatable head avatars. However due to the diversity of frameworks there is no …

The success of style-based generators largely benefits from style modulation, which helps
take care of the cross-instance variation within data. However, theinstance-wise stochasticity …

Abstract 3D facial editing a longstanding task in computer vision with broad applications is
expected to fast and intuitively manipulate any face from arbitrary viewpoints following the …

Generative models are gaining increasing popularity and the demand for precisely
generating images is on the rise. However generating an image that perfectly aligns with …

Generative adversarial networks (GANs) usually struggle in learning from highly diverse
data, whose underlying manifold is complex. In this work, we revisit the mathematical …

Disentanglement learning aims to separate explanatory factors of variation so that different
attributes of the data can be well characterized and isolated, which promotes efficient …