5 citations · 21 across the 61 of their papers we have counts for
17 papers · 1 filter
PrEditor3D: Fast and Precise 3D Shape Editing
Ziya Erkoç, Can Gümeli, Chaoyang Wang +5
We propose a training-free approach to 3D editing that enables the editing of a single shape within a few minutes. The edited 3D mesh aligns well with the prompts, and remains iden…
ZeroKey: Point-Level Reasoning and Zero-Shot 3D Keypoint Detection from Large Language Models
Bingchen Gong, Diego Gomez, Abdullah Hamdi +4
We propose a novel zero-shot approach for keypoint detection on 3D shapes. Point-level reasoning on visual data is challenging as it requires precise localization capability, posin…
Amodal Depth Anything: Amodal Depth Estimation in the Wild
Zhenyu Li, Mykola Lavreniuk, Jian Shi +2
Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene. This task addresses the question of whether models can effectively perceive t…
T-3DGS: Removing Transient Objects for 3D Scene Reconstruction
Alexander Markin, Vadim Pryadilshchikov, Artem Komarichev +3
Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions. To address this challenge, we propose T-3DGS, a novel framework that robustl…
Geometry Distributions
Biao Zhang, Jing Ren, Peter Wonka
Neural representations of 3D data have been widely adopted across various applications, particularly in recent work leveraging coordinate-based networks to model scalar or vector f…
DissolveStereo: Coarse Depth Injection for Zero-Shot Stereo Video Generation
Jian Shi, Qian Wang, Zhenyu Li +3
Generating high-quality stereo videos requires consistent depth perception and temporal coherence across frames. Despite advances in image and video synthesis using diffusion model…
LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion
Biao Zhang, Peter Wonka
This paper introduces a novel hierarchical autoencoder that maps 3D models into a highly compressed latent space. The hierarchical autoencoder is specifically designed to tackle th…
ImmersePro: End-to-End Stereo Video Synthesis Via Implicit Disparity Learning
Jian Shi, Zhenyu Li, Peter Wonka
We introduce \textit{ImmersePro}, an innovative framework specifically designed to transform single-view videos into stereo videos. This framework utilizes a novel dual-branch arch…
Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation
Abdelrahman Eldesokey, Peter Wonka
We propose a diffusion-based approach for Text-to-Image (T2I) generation with interactive 3D layout control. Layout control has been widely studied to alleviate the shortcomings of…
Vivid-ZOO: Multi-View Video Generation with Diffusion Model
Bing Li, Cheng Zheng, Wenxuan Zhu +4
While diffusion models have shown impressive performance in 2D image/video generation, diffusion-based Text-to-Multi-view-Video (T2MVid) generation remains underexplored. The new c…
PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation
Zhenyu Li, Shariq Farooq Bhat, Peter Wonka
This paper introduces PatchRefiner, an advanced framework for metric single image depth estimation aimed at high-resolution real-domain inputs. While depth estimation is crucial fo…
A3D: Does Diffusion Dream about 3D Alignment?
Savva Ignatyev, Nina Konovalova, Daniil Selikhanovych +9
We tackle the problem of text-driven 3D generation from a geometry alignment perspective. Given a set of text prompts, we aim to generate a collection of objects with semantically…
VIA: Unified Spatiotemporal Video Adaptation Framework for Global and Local Video Editing
Jing Gu, Yuwei Fang, Ivan Skorokhodov +4
Video editing serves as a fundamental pillar of digital media, spanning applications in entertainment, education, and professional communication. However, previous methods often ov…
Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models
Qian Wang, Abdelrahman Eldesokey, Mohit Mendiratta +4
We introduce the first zero-shot approach for Video Semantic Segmentation (VSS) based on pre-trained diffusion models. A growing research direction attempts to employ diffusion mod…
PS-CAD: Local Geometry Guidance via Prompting and Selection for CAD Reconstruction
Bingchen Yang, Haiyong Jiang, Hao Pan +3
Reverse engineering CAD models from raw geometry is a classic but challenging research problem. In particular, reconstructing the CAD modeling sequence from point clouds provides g…
LASPA: Latent Spatial Alignment for Fast Training-free Single Image Editing
Yazeed Alharbi, Peter Wonka
We present a novel, training-free approach for textual editing of real images using diffusion models. Unlike prior methods that rely on computationally expensive finetuning, our ap…
AvatarMMC: 3D Head Avatar Generation and Editing with Multi-Modal Conditioning
Wamiq Reyaz Para, Abdelrahman Eldesokey, Zhenyu Li +3
We introduce an approach for 3D head avatar generation and editing with multi-modal conditioning based on a 3D Generative Adversarial Network (GAN) and a Latent Diffusion Model (LD…