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most citedMDepth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation

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cs.CV2026

NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results

Xin Li, Jiachao Gong, Xijun Wang +75

This paper presents an overview of the NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models. This challenge utilizes a new short-form UGC (S-…

cs.CV2025

CtrlVDiff: Controllable Video Generation via Unified Multimodal Video Diffusion

Dianbing Xi, Jiepeng Wang, Yuanzhi Liang +8

We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g.…

cs.CV2025

OmniVDiff: Omni Controllable Video Diffusion for Generation and Understanding

Dianbing Xi, Jiepeng Wang, Yuanzhi Liang +5

In this paper, we propose a novel framework for controllable video diffusion, OmniVDiff , aiming to synthesize and comprehend multiple video visual content in a single diffusion mo…

cs.CV2025

NFIG: Multi-Scale Autoregressive Image Generation via Frequency Ordering

Zhihao Huang, Xi Qiu, Yukuo Ma +5

Autoregressive models have achieved significant success in image generation. However, unlike the inherent hierarchical structure of image information in the spectral domain, standa…

cs.CV2024

VAST 1.0: A Unified Framework for Controllable and Consistent Video Generation

Chi Zhang, Yuanzhi Liang, Xi Qiu +2

Generating high-quality videos from textual descriptions poses challenges in maintaining temporal coherence and control over subject motion. We propose VAST (Video As Storyboard fr…

cs.CV20241 cited

MDepth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation

Yingshuang Zou, Yikang Ding, Xi Qiu +2

This paper presents a novel self-supervised two-frame multi-camera metric depth estimation network, termed MDepth, which is designed to predict reliable scale-aware surroundi…