We introduce SceneDiffuser, a conditional generative model for 3D scene understanding.
SceneDiffuser provides a unified model for solving scene-conditioned generation …

We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

We introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework,
for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to …

Estimating 3D humans from images often produces implausible bodies that lean, float, or
penetrate the floor. Such methods ignore the fact that bodies are typically supported by the …

This paper presents a framework for efficient 3D clothed avatar reconstruction. By combining
the advantages of the high accuracy of optimization-based methods and the efficiency of …

With the progress of 3D human pose and shape estimation, state-of-the-art methods can
either be robust to occlusions or obtain pixel-aligned accuracy in non-occlusion cases …

Capturing the interactions between humans and their environment in 3D is important for
many applications in robotics, graphics, and vision. Recent works to reconstruct the 3D …

Although the estimation of 3D human pose and shape (HPS) is rapidly progressing, current
methods still cannot reliably estimate moving humans in global coordinates, which is critical …

We introduce TADA, a simple-yet-effective approach that takes textual descriptions and
produces expressive 3D avatars with high-quality geometry and lifelike textures, that can be …

We present SLOPER4D, a novel scene-aware dataset collected in large urban
environments to facilitate the research of global human pose estimation (GHPE) with human …