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cs.CV2026

ProxyUp: Training-Free Proxy-Conditioned Video Generation for Controllable Dynamics

Zanwei Zhou, Jiazhong Cen, Jiemin Fang +7

Precise control over complex dynamics remains challenging for modern video generative models, as text prompts alone often cannot specify physically plausible, fine-grained motion a…

cs.CV2026

UniCSG: Unified High-Fidelity Content-Constrained Style-Driven Generation via Staged Semantic and Frequency Disentanglement

Jingwei Yang, Ruoxi Wu, Wei Shen +4

Style transfer must match a target style while preserving content semantics. DiT-based diffusion models often suffer from content-style entanglement, leading to reference-content l…

cs.CV2026

Towards In-Context Tone Style Transfer with A Large-Scale Triplet Dataset

Yuhai Deng, Huimin She, Wei Shen +4

Tone style transfer for photo retouching aims to adapt the stylistic tone of the reference image to a given content image. However, the lack of high-quality large-scale triplet dat…

cs.CV2026

RefReward-SR: LR-Conditioned Reward Modeling for Preference-Aligned Super-Resolution

Yushuai Song, Weize Quan, Weining Wang +8

Recent advances in generative super-resolution (SR) have greatly improved visual realism, yet existing evaluation and optimization frameworks remain misaligned with human perceptio…

cs.CV2026

Text-Image Conditioned 3D Generation

Jiazhong Cen, Jiemin Fang, Sikuang Li +8

High-quality 3D assets are essential for VR/AR, industrial design, and entertainment, motivating growing interest in generative models that create 3D content from user prompts. Mos…

cs.CV2025

WorldGrow: Generating Infinite 3D World

Sikuang Li, Chen Yang, Jiemin Fang +6

We tackle the challenge of generating the infinitely extendable 3D world -- large, continuous environments with coherent geometry and realistic appearance. Existing methods face ke…