Abstract This work presents Depth Anything a highly practical solution for robust monocular
depth estimation. Without pursuing novel technical modules we aim to build a simple yet …

Diffusion models (DMs) have become the new trend of generative models and have
demonstrated a powerful ability of conditional synthesis. Among those, text-to-image …

Understanding and reasoning about spatial relationships is crucial for Visual Question
Answering (VQA) and robotics. Vision Language Models (VLMs) have shown impressive …

This paper presents a method to optimize Gaussian splatting with a limited number of
images while avoiding overfitting. Representing a 3D scene by combining numerous …

Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to
produce metric predictions. Even so, the resulting models will be geometry-specific, with …

Recent advances in large-scale pretraining have yielded visual foundation models with
strong capabilities. Not only can recent models generalize to arbitrary images for their …

With the widespread usage of VR devices and contents, demands for 3D scene generation
techniques become more popular. Existing 3D scene generation models, however, limit the …

This paper reports on the NTIRE 2024 challenge on HR Depth From images of Specular and
Transparent surfaces held in conjunction with the New Trends in Image Restoration and …

We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis
for in-the-wild scenes. While existing methods are designed for single objects with masked …

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 …