paperlist.ai

CVPR 2023 Papers

Conference on Computer Vision and Pattern Recognition

202620252024202320222021202020192018201720162015

Decision breakdown

2,359papers

Counted from the official paper list; may differ slightly from the number the conference announced.

  • Poster
    2,13690.5%
  • Other / unlabeled
    2239.5%

Top 10 topics (click to filter)

Trends for all 207 research topics

Browse all papers

All2,359Poster2,136
TryOnDiffusion: A Tale of Two UNets

Luyang Zhu, Dawei Yang, Tyler Zhu, Fitsum Reda, William Chan, Chitwan Saharia, Mohammad Norouzi, Ira Kemelmacher-Shlizerman

Expand abstract

Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of the garment, while warping the garment to accommodate a significant body pose and shape change across the subjects. Previous methods either focus on garment detail preservation without effective pose and shape variation, or allow try-on with the desired shape and pose but lack garment details. In this paper, we propose a diffusion-based architecture that unifies two UNets (referred to as Parallel-UNet), which allows us to preserve garment details and warp the garment for significant pose and body change in a single network. The key ideas behind Parallel-UNet include: 1) garment is warped implicitly via a cross attention mechanism, 2) garment warp and person blend happen as part of a unified process as opposed to a sequence of two separate tasks. Experimental results indicate that TryOnDiffusion achieves state-of-the-art performance both qualitatively and quantitatively.

Poster
Diffusion ModelsGenomics
In-Hand 3D Object Scanning From an RGB Sequence

Shreyas Hampali, Tomas Hodan, LUAN TRAN, Lingni Ma, Cem Keskin, Vincent Lepetit

Expand abstract

We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneously optimize both the object shape and the pose trajectory. As direct optimization over all shape and pose parameters is prone to fail without coarse-level initialization, we propose an incremental approach that starts by splitting the sequence into carefully selected overlapping segments within which the optimization is likely to succeed. We reconstruct the object shape and track its poses independently within each segment, then merge all the segments before performing a global optimization. We show that our method is able to reconstruct the shape and color of both textured and challenging texture-less objects, outperforms classical methods that rely only on appearance features, and that its performance is close to recent methods that assume known camera poses.

Poster
Object Detection3D Vision