We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework that enables generative diffusion bridge modeling with fractional noise for both paired and unpaired training data.

Representational alignment based on concept discovery across ViTs trained on different tasks.

We propose a new 3D Gaussian splatting GAN for 3D consistent rendering at high resolution