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20242026
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cs.CV2024

PartGen: Part-level 3D Generation and Reconstruction with Multi-View Diffusion Models

Minghao Chen, Roman Shapovalov, Iro Laina +4

Text- or image-to-3D generators and 3D scanners can now produce 3D assets with high-quality shapes and textures. These assets typically consist of a single, fused representation, l…

cs.CV2024

DGE: Direct Gaussian 3D Editing by Consistent Multi-view Editing

Minghao Chen, Iro Laina, Andrea Vedaldi

We consider the problem of editing 3D objects and scenes based on open-ended language instructions. A common approach to this problem is to use a 2D image generator or editor to gu…

cs.CV2024

3D-Aware Instance Segmentation and Tracking in Egocentric Videos

Yash Bhalgat, Vadim Tschernezki, Iro Laina +3

Egocentric videos present unique challenges for 3D scene understanding due to rapid camera motion, frequent object occlusions, and limited object visibility. This paper introduces…

cs.CV2024

Diffusion Models for Open-Vocabulary Segmentation

Laurynas Karazija, Iro Laina, Andrea Vedaldi +1

Open-vocabulary segmentation is the task of segmenting anything that can be named in an image. Recently, large-scale vision-language modelling has led to significant advances in op…

cs.CV2024

Rethinking Image Super-Resolution from Training Data Perspectives

Go Ohtani, Ryu Tadokoro, Ryosuke Yamada +7

In this work, we investigate the understudied effect of the training data used for image super-resolution (SR). Most commonly, novel SR methods are developed and benchmarked on com…

cs.CV2024

Splatt3R: Zero-shot Gaussian Splatting from Uncalibrated Image Pairs

Brandon Smart, Chuanxia Zheng, Iro Laina +1

In this paper, we introduce Splatt3R, a pose-free, feed-forward method for in-the-wild 3D reconstruction and novel view synthesis from stereo pairs. Given uncalibrated natural imag…