My interests lie in the intersection of computer vision, computer graphics, and machine learning,
particularly in inverse graphics powered by neural rendering,
including 3D generative models, shape analysis and 3D avatar, etc.
Gaussian3Diff adopts 3D Gaussians defined in UV space as the underlying 3D
representation, which intrinsically support high-quality novel view synthesis, 3DMM-based animation
and 3D diffusion for unconditional generation.
We propose a novel face reenactment framework,
which adopts tri-planes as fundamental NeRF representation and decomposes face tri-planes into three components: canonical tri-planes, identity deformations, and motion.
We study dense correspondence, which plays a key role in 3D scene understanding but has been ignored in NeRF
DDF presents a novel way to distill dense NeRF correspondence from pre-trained NeRF GAN