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20242026
most citedDGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images

1 citations · 1 across the 6 of their papers we have counts for

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cs.CV2026

LibraGen: Playing a Balance Game in Subject-Driven Video Generation

Jiahao Zhu, Shanshan Lao, Lijie Liu +10

With the advancement of video generation foundation models (VGFMs), customized generation, particularly subject-to-video (S2V), has attracted growing attention. However, a key chal…

cs.CV2026

FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation

Gen Li, Peiyu Liu

Vision-Language Models (VLMs) excel at visual reasoning but still struggle with integrating external knowledge. Retrieval-Augmented Generation (RAG) is a promising solution, but cu…

cs.CV2025

DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation

Jiawei Liu, Junqiao Li, Jiangfan Deng +11

The "one-shot" technique represents a distinct and sophisticated aesthetic in filmmaking. However, its practical realization is often hindered by prohibitive costs and complex real…

cs.CV2025

Seedance 1.5 pro: A Native Audio-Visual Joint Generation Foundation Model

Team Seedance, Heyi Chen, Siyan Chen +194

Recent strides in video generation have paved the way for unified audio-visual generation. In this work, we present Seedance 1.5 pro, a foundational model engineered specifically f…

cs.CV20251 cited

DGGT: Feedforward 4D Reconstruction of Dynamic Driving Scenes using Unposed Images

Xiaoxue Chen, Ziyi Xiong, Yuantao Chen +11

Autonomous driving needs fast, scalable 4D reconstruction and re-simulation for training and evaluation, yet most methods for dynamic driving scenes still rely on per-scene optimiz…

cs.CV2025

OmniInsert: Mask-Free Video Insertion of Any Reference via Diffusion Transformer Models

Jinshu Chen, Xinghui Li, Xu Bai +8

Recent advances in video insertion based on diffusion models are impressive. However, existing methods rely on complex control signals but struggle with subject consistency, limiti…