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20242026
most citedBenchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning

2 citations · 2 across the 14 of their papers we have counts for

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cs.CV2026

Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction

Yujie Wei, Chenglong Ma, Jianxiong Gao +6

Reconstructing dynamic visual experiences as videos from functional magnetic resonance imaging (fMRI) is pivotal for advancing the understanding of neural processes. However, curre…

cs.CV2026

CoDance: An Unbind-Rebind Paradigm for Robust Multi-Subject Animation

Shuai Tan, Biao Gong, Ke Ma +5

Character image animation is gaining significant importance across various domains, driven by the demand for robust and flexible multi-subject rendering. While existing methods exc…

cs.CV2026

PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models

Qiyuan Zhang, Biao Gong, Shuai Tan +7

Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation. This gap highlights a critical limitat…

cs.CV2025

Animate-X++: Universal Character Image Animation with Dynamic Backgrounds

Shuai Tan, Biao Gong, Zhuoxin Liu +4

Character image animation, which generates high-quality videos from a reference image and target pose sequence, has seen significant progress in recent years. However, most existin…

cs.CV2025

MotionStrata: Hierarchical Motion Latents for Compact Video Autoencoding

Wenzhang Sun, Huaize Liu, Chunfeng Wang +3

First-frame-conditioned video autoencoders represent a clip with persistent content and a compact motion code. Although this removes much of the appearance redundancy, the remainin…

cs.CV2025

DreamRelation: Relation-Centric Video Customization

Yujie Wei, Shiwei Zhang, Hangjie Yuan +8

Relational video customization refers to the creation of personalized videos that depict user-specified relations between two subjects, a crucial task for comprehending real-world…