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20242026
most citedFitDiT: Advancing the Authentic Garment Details for High-fidelity Virtual Try-on

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cs.CV2026

Evidence-RL: Towards Evidence-intensive Visual Reasoning

Haojie Huang, Xinlei Yu, Chengming Xu +6

Vision-Language Models (VLMs) should answer from concrete image evidence rather than language priors, dataset shortcuts, or irrelevant visual context. Existing perception-aware pos…

cs.CV2026

SPOT-E: Test-Time Entropy Shaping with Visual Spotlights for Frozen VLMs

Bo Yin, Xiaobin Hu, Chengming Xu +6

Vision-language models (VLMs) often underperform on evidence intensive tasks because decisive visual evidence are small, localized, and easy to overlook, leading to failures in evi…

cs.CV2026

What Semantics Survive the Connector? Diagnosing VLM-to-DiT Alignment in Video Editing

Hangyu Lin, Chao Wen, Chengming Xu +4

Flow matching based video generative models have been increasingly relying on prepended Vision-Language Models (VLMs) to handle complex, instruction-based video editing. The prevai…

cs.CV2026

PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset

Haojun Chen, Haoyang He, Chengming Xu +11

Text-to-Image (T2I) models have recently seen notable progress around 1K and 2K resolution. With the extreme desire for better visual experience and the rapid development of imagin…

cs.CV2026

FFP-300K: Scaling First-Frame Propagation for Generalizable Video Editing

Xijie Huang, Chengming Xu, Donghao Luo +6

First-Frame Propagation (FFP) offers a promising paradigm for controllable video editing, but existing methods are hampered by a reliance on cumbersome run-time guidance. We identi…

cs.CV2025

LongVie 2: Multimodal Controllable Ultra-Long Video World Model

Jianxiong Gao, Zhaoxi Chen, Xian Liu +7

Building video world models upon pretrained video generation systems represents an important yet challenging step toward general spatiotemporal intelligence. A world model should p…