Intrinsic images, in the original sense, are image-like maps of scene properties like depth,
normal, albedo, or shading. This paper demonstrates that StyleGAN can easily be induced …

Comprehensive 3D scene understanding, both geometrically and semantically, is important
for real-world applications such as robot perception. Most of the existing work has focused …

Generative models excel at mimicking real scenes, suggesting they might inherently encode
important intrinsic scene properties. In this paper, we aim to explore the following key …

Multi-task visual learning is a critical aspect of computer vision. Current research, however,
predominantly concentrates on the multi-task dense prediction setting, which overlooks the …

We investigate how to generate multimodal image outputs, such as RGB, depth, and surface
normals, with a single generative model. The challenge is to produce outputs that are …

Multi-task learning has become increasingly popular in the machine learning field but its
practicality is hindered by the need for large labeled datasets. Most multi-task learning …

Representation disentanglement may help AI fundamentally understand the real world and
thus benefit both discrimination and generation tasks. It currently has at least three …

There are two major approaches to building good machine learning algorithms: feeding lots
of data into large models or picking a model class with an “inductive bias” that suits the …

Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising
results in dense visual perception tasks. However, most existing work treats diffusion models …

Vision Foundation Models (VFMs) have demonstrated outstanding performance on
numerous downstream tasks. However, due to their inherent representation biases …